Introduction

(to be written) ## DFO Salmon Network reflects the International Year of the Salmon (to be written)
## Surveys for the DFO Salmon Network
The survey was named DFO Salmon Net: People and Projects with 351 selected recipients from within DFO. Recipients were contacted by email 2017 September and October. A PDF of the survey questions is posted HERE. A .csv file with 163 responses was downloaded from Survey Monkey after the survey was closed 2017 October 29. Many responses were incomplete.

Survey Data

Raw Data and Ancillary Data

The raw survey data is HERE. Hand edits were required to reorganize information into the appropriate fields, create comma separated lists for complicated results (e.g. lists of URLs, items with commas in quotes, fix spelling, expand abbreviations, standardize capital letters (fewer), and remove spurious text. The resulting sheet is HERE.

The survey results are in condensed format and replaces the text for multiple choice answers with numeric codes referring to the user’s choice, with the translations in yet more Google Sheets: Codes, Place Address and IYS Themes and Topics. Supplementary data, not obtained from the survey, includes Person Details.

All the data is held in Google Sheets to support collaboration and prevent loss. Sheets have roll-back, so all preceding versions (automated) can be recovered.

Read IYS topics and choices from Google Sheets

Survey choices for the IYS topics
code long short
1 No, or not applicable no
2 Yes, but unlikely at present pending
3 Yes, I have an activity that would benefit from additional collaboration activities to share
4 Yes, I am keen to share data, skills, and/or knowledge with other knowledge to share
5 Yes, this collaboration is vital to my work and should be a high priority for DFO vital topic

Read “Edited Survey 2017 October 29”

Edited Survey 2017 October 29
email firstName lastName webPage jobTitle jobDescription branchDirectorateSector locationCode locationOther IYS6.99 activity2Title activity3Title
65 Beth.Lenentine@dfo-mpo.gc.ca Beth Lenentine NA NA NA NA NA NA NA NA NA
104 John.Holmes@dfo-mpo.gc.ca John Holmes NA Division Manager, Aquatic Resources, Research and Assessment Division aka Stock Assessment Division Science Branch, “Aquatic Resources, Research, and Assessment Division”, Ecosystems and Oceans Science 12 NA NA NA NA
114 Laura.Brown@dfo-mpo.gc.ca Laura Brown http://www.cowichanwatershedboard.ca/topcat/about, http://www.nserc-crsng.gc.ca/Students-Etudiants/PD-NP/Laboratories-Laboratoires/FO-PO_eng.asp South Coast Area Director NA Fisheries and Aquaculture Management Division 18 NA NA NA NA
119 Lyse.Godbout@dfo-mpo.gc.ca Lyse Godbout NA Research Biologist NA Science Branch,“Aquatic Resources, Research, and Assessment Division”, Quantitative Assessment Methods Section 12 NA NA Size-selective Mortality and Early Marine Growth Climate and Juvenile Salmon
126 Mary.Thiess@dfo-mpo.gc.ca Mary Thiess NA NA Acting Head, Salmon Assessment Science Branch, “Aquatic Resources, Research, and Assessment Division”, Salmon Assessment 12 NA NA NA NA

Read Person with Region and Job Code

This ancillary table has more names than the survey recipients.

Person with Region and Job Type
name region jobCode telephone place
17 Andrew Pereboom Pacific EG 250-286-5884 Campbell River
60 Carmel Lowe Pacific MA NA NA
85 Craig Keddy Maritimes HA 902-679-5572 NA
153 Ivan Winther Pacific BI 250-627-3459 Prince Rupert Field Office
158 Jamie Scroggie Pacific RM 250-851-4878 NA
193 Julia Bradshaw Pacific EG 250-756-7054 NA
260 Michael Folkes Pacific BI 250-756-7264 NA
294 Peter Hall Pacific RM 250-720-4445 NA
303 Rick Rempel Pacific EG 604-666-0691 NA
308 Rob Schaefer Pacific HA 604-814-1070 NA
Recipiets SalmonNet 2017 September26
code shortJobName longJobName
MA Manager Manager (people manager, not resource/fishery manager)
EG Technician Technical (technician, engineer)
RM Resource Manager Resource Manager (includes biologists and tech’s whose primary role is wrt fisheries management
PO Policy or Economics Policy analyst, economist
BI Biologist Biologist (science)
RE Research Scientist Research Scientist
HA Enhancement Biologists, community advisors, managers and technicians working on enhancement
## Read Recipients
Obtaine d from Survey Monkey, people who actually received the survey and two reminders.
name email responded
1 Aaron Burgoyne Aaron.Burgoyne@dfo-mpo.gc.ca No
53 Carlos Martinez Carlos.Martinez@dfo-mpo.gc.ca No
84 Darren Goetze Darren.Goetze@dfo-mpo.gc.ca No
105 Eddy Kennedy Eddy.Kennedy@dfo-mpo.gc.ca Complete
112 Frank Corbett Frank.Corbett@dfo-mpo.gc.ca Complete
163 Joan Bennett Joan.Bennett@dfo-mpo.gc.ca No
215 Les Clint Les.Clint@dfo-mpo.gc.ca Partial
246 Merv Mochizuki Merv.Mochizuki@dfo-mpo.gc.ca No
282 Pieter Van Will Pieter.VanWill@dfo-mpo.gc.ca No
302 Sandy Devcic Sandra.Devcic@dfo-mpo.gc.ca Complete

Extract Names for IYS Theme and Topic

Lists of theme and topic names, and a variable fctr that relates the 37 topics to the 6 themes.

j <- IYSCodeIdea$IYS_Row == 0  # theme
print("Theme, Short")
theme=IYSCodeIdea[j,"IYS_Short_Text"] %T>% print;
j <- j | IYSCodeIdea$IYS_Row == 99 # theme or "other"
print("Topic, Short")
topic <- IYSCodeIdea[!j,"IYS_Short_Text"]  %T>% print; # not theme, not "other"
print("\nRelate 37 Topics to 6 Themes")
fctr <- IYSCodeIdea[!j,"IYS_Theme"] %T>% print 
[1] "Theme, Short"
[1] "IYS.1 Status Salmon and Habitats"  
[2] "IYS.2 Effects of Changing Habitats"
[3] "IYS.3 New tech and methods"        
[4] "IYS.4 Connecting Salmon to People" 
[5] "IYS.5  Information Systems"        
[6] "IYS.6 Outreach and Communication"  
[1] "Topic, Short"
 [1] "IYS.1.1 Field Data"                          
 [2] "IYS.1.2 Data Analysis"                       
 [3] "IYS.1.3 Fishery Management, Assessment"      
 [4] "IYS.1.4 Stock Status Assessment"             
 [5] "IYS.1.5 Habitat Assessment"                  
 [6] "IYS.1.6 Population identification"           
 [7] "IYS.1.7 Marine Survival, Growth, Migration"  
 [8] "IYS.1.8 Interactions: Wild, Hatchery, Farmed"
 [9] "IYS.1.9 Toxicology"                          
[10] "IYS.2.1 Freshwater habitats"                 
[11] "IYS.2.2 Marine and Estuarine Habitats"       
[12] "IYS.2.3 Climate and Ecosystem Models"        
[13] "IYS.2.4 Adaptation"                          
[14] "IYS.2.5 Policy and Management"               
[15] "IYS.3.1 Field methods"                       
[16] "IYS.3.2 Individual fish"                     
[17] "IYS.3.3 Fisheries management process"        
[18] "IYS.3.4 New analyses"                        
[19] "IYS.3.5 Advances genetics, genomics"         
[20] "IYS.3.6 Science management"                  
[21] "IYS.3.7 Implementation"                      
[22] "IYS.4.1 First Nations Opportunities"         
[23] "IYS.4.2 Benefits from Salmon"                
[24] "IYS.4.3 Community engagement"                
[25] "IYS.4.4 Better science communication"        
[26] "IYS.4.5 Traditional ecological knowledge"    
[27] "IYS.4.6 Young scientists"                    
[28] "IYS.4.7 Changing role of salmon in societies"
[29] "IYS.5.1 Database Integration"                
[30] "IYS.5.2 Knowledge management"                
[31] "IYS.5.3 Data sharing arrangements"           
[32] "IYS.5.4 Data visualization"                  
[33] "IYS.6.1 International projects"              
[34] "IYS.6.2 Celebrating success"                 
[35] "IYS.6.3 Outreach methods, awareness"         
[36] "IYS.6.4 Engagement FM to science to FM"      
[37] "IYS.6.5 Linking salmon to climate change"    
[1] "\nRelate 37 Topics to 6 Themes"
 [1] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 6 6 6
[36] 6 6

Merge Person Details with Recipients

A list of survey recipients was recovered from Survey Monkey, provided name, email, and response (complete,partial,no) for 351 recipients and 163 responses. A separate list of 368 DFO salmon staff was compiled with name, email, region, and job type (7 categories). These lists were merged. Misspelt names were discovered and corrected in the longer staff list.

# person is 367 by 9. emails are all in caps, unlike recipient
# recipient is 351 by 3.
person1 = merge(x=person[,c(1,2,4)], y=recipient, by="name",all.y=TRUE); # 351
person1$email.x <- NULL; # remove a column
x=person1[ (is.na(person1$region) | is.na(person1$jobCode)), ]; 
if(nrow(x) != 0) {print(x[1:max(nrow(x),10)])} else print("No job or region missing") # check!
[1] "No job or region missing"

Region and Job as Factors for Survey

The survey data with 163 names was merged with ancilliary data for 351 names, using email address as the unique identifier in both tables. Variables name,region, and jobCode are merged into survey.

Capital letters in emails vary between lists: person has names as proper nouns but not all emails in survey have names with capital letters. This was solved by merging person with recipient so person1 had the same capitals for email as survey.

# survey=survey[order(survey$email),]    # sort, vary caps in email have no effect
# person1=person1[order(person1$email),] # 351 rows sort ditto
survey1=merge(survey,person1[,c(1:4)],by="email",all.x=T ) # 163 rows
# not all emails in survey are capitalized.
# write.csv(file="test.csv",survey1[,c(1:3,76:78)]) # checked!

Analysis

Determine Thorough Answers re IYS Topics

First determine columns for choices re IYS theme and topic, then count missing answers for IYS topics for each response. That results is used for the frequency of responses by job type and region. x0 is a matrix, 163 by 37.
x1 is 124 by 37 after deleting not-useful responses, nar. y0 is a data.frame,163 by 40 after adding columns name, region, jobType.

k = colnames(survey1) %>% substr(1,3) %>% equals("IYS"); # cols 10 to 52, so 43
k1= colnames(survey1) %>% substr(5,7) %>% equals(".99"); # 6 of IYS "other"
k=k & !k1;                        # leaves 37 columns
x0 = survey1[ ,k] %>% as.matrix;  # 163 row, 37 col
y0 <- cbind(x0, survey1[76:78]) # add name,region,job to IYS choices
nar = apply(x0,1, function(x) sum(is.na(x)) ) # count missing by person (row)
jans <-  nar > (37-25) # rows with < 25 answers to 37 topics, junk answers
thorough = rep("Thorough",163);
thorough[jans] <- "Partial";  # 39, leaves 124
x1 <- x0[!jans,] # useful choices
y1 <- y0[!jans,] # with factors
nac <- x1 %>% apply(2, function(x) sum(is.na(x)) ) # count missing by topic  
names(nac)= colnames(x0)
nac
IYS1.1 IYS1.2 IYS1.3 IYS1.4 IYS1.5 IYS1.6 IYS1.7 IYS1.8 IYS1.9 IYS2.1 
     0      3      2      2      2      2      4      4      4      0 
IYS2.2 IYS2.3 IYS2.4 IYS2.5 IYS3.1 IYS3.2 IYS3.3 IYS3.4 IYS3.5 IYS3.6 
     1      2      2      1      0      2      2      2      2      1 
IYS3.7 IYS4.1 IYS4.2 IYS4.3 IYS4.4 IYS4.5 IYS4.6 IYS4.7 IYS5.1 IYS5.2 
     2      1      0      1      0      1      1      2      1      0 
IYS5.3 IYS5.4 IYS6.1 IYS6.2 IYS6.3 IYS6.4 IYS6.5 
     0      0      1      0      0      1      0 

Compare Responders to Recipients by Job Type

The survey was sent to 351 people, of which 163 responded and 124 provided useful choices about collaboration on topics within IYS themes. The job types for recipients were counted and compared to responders, and similarly for DFO regions. The 351 responses also had to have a count by person (row) of missing choices re IYS topics to determine “not thorough” responses.

rrp=c("Recipients","Responders","Thorough","Percent")
responded <- person1$responded
responded[responded == "Opted out"] <- "No"
responded[ person1$responded== "Partial" | person1$responded == "Complete"] <- "Yes";
# nar is 163 long from survey responses not 351 from recipients.
jj <- 0;
for(j in 1:length(responded)){  # 351
    if(responded[j] == "Yes") {
        jj=jj+1;
        responded[j] <- thorough[jj] # 163 things in 351 places 
    }
}
jobFreq  <- table(person1$jobCode, responded) %>% addmargins
a <- as.matrix(jobFreq)
PercentThorough <- round( 100*a[,3]/a[,4], 0) 
bj=cbind(a,PercentThorough)
kable(bj,caption="Table x. Survey Response by Job Type across all Regions")
Table x. Survey Response by Job Type across all Regions
No Partial Thorough Sum PercentThorough
BI 35 8 27 70 39
EG 53 9 21 83 25
HA 17 3 16 36 44
MA 18 6 11 35 31
PO 9 1 5 15 33
RE 8 4 9 21 43
RM 48 8 35 91 38
Sum 188 39 124 351 35

Compare Responders to Recipients by Region

region = person1$region
region[region == "Newfoundland"] <- "NL";
region[region == "Central and Arctic"] <- "Central";
region[region == "HQ"] <- "NCR";
regFreq  = table(region,responded) %>% addmargins
a <- as.matrix(regFreq)
PercentThorough <- round( 100*a[,3]/a[,4], 0) 
br=cbind(a,PercentThorough)
kable(br,caption="Table x. Survey Response by Region across all Job Types")
Table x. Survey Response by Region across all Job Types
No Partial Thorough Sum PercentThorough
Central 1 0 1 2 50
Gulf 12 2 7 21 33
Maritimes 25 6 12 43 28
NCR 3 0 2 5 40
NL 5 2 4 11 36
Pacific 140 28 94 262 36
Quebec 2 1 4 7 57
Sum 188 39 124 351 35

Summarize as plots.

SetPar(); par(xaxs="r",mgp = c(2,0.5,0))
colr=c("grey","cyan","magenta")
txt=c("Thorough","Partial","No")
barplot(t(jobFreq[1:7,1:3]), col=colr, beside=F, las=1, ylim=c(0,100), 
        xlab="Job Type", ylab="Count"); 
    box();axis(4,labels=F)
legend("top",legend=txt, fill=rev(colr), bty="n")
Survey recipients and responders compared by job type. MA: Manager (primarily manage staff), EG: Technicians and Engineers (not in hatcheries), RM: Resource Manager (fisheries and habitat management by biologists, technicians, and managers), PO: Policy analysts and economists, BI: Biologist (Science Branch), RE: Research Scientist, HA: Enhancement Staff (biologists, community advisors, hatcheries staff).

Survey recipients and responders compared by job type. MA: Manager (primarily manage staff), EG: Technicians and Engineers (not in hatcheries), RM: Resource Manager (fisheries and habitat management by biologists, technicians, and managers), PO: Policy analysts and economists, BI: Biologist (Science Branch), RE: Research Scientist, HA: Enhancement Staff (biologists, community advisors, hatcheries staff).

SetPar(); par(xaxs="r",mgp = c(2,0.5,0))
barplot(t(regFreq[1:7,1:3]), col=colr, beside=F, las=2, ylim=c(0,275),
    xlab="", ylab="Count"); 
    box();axis(4,labels=F)
legend("top",legend=txt, fill=rev(colr), bty="n")
Survey recipients and responders compared by DFO Region. Central: Central and Arctic, NCR: National Capital Region, NFLD: Newfoundland and Labrador,

Survey recipients and responders compared by DFO Region. Central: Central and Arctic, NCR: National Capital Region, NFLD: Newfoundland and Labrador,

Survey Code Substitution

Survey choices about IYS topics can be expanded to short names and long names for displays. Similarly, codes for job, region, and location (building) can be expanded. A function to determine the correct row of names in IYSCodeIdea is required. The actual choice, 1 to 5, is the row in IYSCodeChoice. If all IYS choices were missing, that response was deleted. If some choices were missing, lack of interest was assumed (assigned choice = 1).

# count missing in each column
nar = x0 %>% apply(1, function(x) sum(is.na(x)) )
  # count missing in each row
jans = nar > (37-25) # rows with < 25 answers to 37 topics, junk answers

Examination of pattern of skips.

For IYS topics, 34 survey responders did not address any IYS topics" and 39 addressed fewer than 25 topics. Those responders were deleted before the analysis of collaboration potential.

Table x. Frequency of skipped choices by IYS topic.

a=tapply(nac,theme[fctr],sum)  # sum missing by theme for the 37 topics
b0 <- table(fctr)
b1=(a / b0) %>% round(.,1) # skips per topic by theme
b2=cbind(a,b0,b1) 
colnames(b2) <- c("Skips","Topics","Skips/Topics")
kable(b2, caption ="Table x. Frequency of skipped choices by IYS topic.")
Table x. Frequency of skipped choices by IYS topic.
Skips Topics Skips/Topics
IYS.1 Status Salmon and Habitats 23 9 2.6
IYS.2 Effects of Changing Habitats 6 5 1.2
IYS.3 New tech and methods 11 7 1.6
IYS.4 Connecting Salmon to People 6 7 0.9
IYS.5 Information Systems 1 4 0.2
IYS.6 Outreach and Communication 2 5 0.4

Interpretation of Effect of Skips

Responders who made choices for 25 or more, but not all, of the IYS topics did not tend to quit before the last themes. The opposite is true, there were more skips in the first theme than in subsequent themes. The skips were spread evenly across the topics within theme 1 and theme 3 (34 of the 49 skips). From 124 useful resposes to 37 topic, there are 4,588 choices, of which 49 skips is 1%. We concluded that the pattern of skips would not introduce an important bias if we were wrong about interpreting a skip to mean that a topic was No, or not applicable to a responder’s interest in potential collaborations, i.e., that a skip is the same as choice = 1. In this situation, not making a choice was a meaningful choice.

Barplot of skips from useful but incomplete choices about IYS topics.

x=nac[j <- nac > 0] # skips in topics where there were skips
SetPar();par(xaxs="r",oma = c(3,2,1,1));
barplot(x, names.arg=topic[j],las=2,ylim=c(0,5),ylab="Count",xlab="",cex.names = 0.33)
box();
Count of skips for IYS Topics where skips occurred, within the 124 survey responses considered useful.

Count of skips for IYS Topics where skips occurred, within the 124 survey responses considered useful.

x=nac #
SetPar();par(xaxs="r",oma = c(3,2,1,1));
barplot(x, names.arg=topic,las=2,ylim=c(0,5),ylab="Count",xlab="",cex.names = 0.33)
box();
Count of skips for IYS Topics where skips occurred, including topics with zero skips, within the 124 survey responses considered useful.

Count of skips for IYS Topics where skips occurred, including topics with zero skips, within the 124 survey responses considered useful.

x=nac # total choice in topics including topics with zero skips
SetPar();par(xaxs="r",oma = c(3,2,1,1));
barplot(124-x, names.arg=topic,las=2,ylim=c(0,124),
        ylab="Count",xlab="",cex.names = 0.33); box();
Totals for choice in topics, within the 124 survey responses considered useful.

Totals for choice in topics, within the 124 survey responses considered useful.

Summary of Choices for IYS Topics.

A table with the count of choices for each topic is created (37 rows, 5 columns) by a local function ChoiceTabSum.

x1[is.na(x1)] <- 1;  # skipped -> Choice 1, "not applicable" 
y1[is.na(y1)] <- 1;
x2 = x1 %>% apply(2,ChoiceTabSum); # by column. 5 by 37 
colnames(x2) <- topic
x2 <- t(x2); # a matrix, 37 by 5
choice=c("1.  No","2. Pending","3. Activities","4. Knowledge","5. Vital"); 
kable(x2, col.names=choice, caption="Counts of choices regarding collaboration by IYS topic, from 124 thorough responses."); 
Terse(x2) # to convert text to table in Word.
Counts of choices regarding collaboration by IYS topic, from 124 thorough responses.
1. No 2. Pending 3. Activities 4. Knowledge 5. Vital
IYS.1.1 Field Data 20 19 29 23 33
IYS.1.2 Data Analysis 19 29 26 30 20
IYS.1.3 Fishery Management, Assessment 25 23 31 20 25
IYS.1.4 Stock Status Assessment 30 31 27 22 14
IYS.1.5 Habitat Assessment 35 37 22 16 14
IYS.1.6 Population identification 37 25 28 16 18
IYS.1.7 Marine Survival, Growth, Migration 33 16 33 17 25
IYS.1.8 Interactions: Wild, Hatchery, Farmed 36 28 23 14 23
IYS.1.9 Toxicology 67 28 20 5 4
IYS.2.1 Freshwater habitats 29 36 29 16 14
IYS.2.2 Marine and Estuarine Habitats 28 40 28 12 16
IYS.2.3 Climate and Ecosystem Models 33 42 29 11 9
IYS.2.4 Adaptation 32 44 23 13 12
IYS.2.5 Policy and Management 31 45 21 17 10
IYS.3.1 Field methods 21 31 26 23 23
IYS.3.2 Individual fish 36 30 25 20 13
IYS.3.3 Fisheries management process 33 36 23 18 14
IYS.3.4 New analyses 38 37 26 14 9
IYS.3.5 Advances genetics, genomics 46 29 28 12 9
IYS.3.6 Science management 31 30 30 19 14
IYS.3.7 Implementation 22 24 36 27 15
IYS.4.1 First Nations Opportunities 18 30 31 18 27
IYS.4.2 Benefits from Salmon 24 42 26 13 19
IYS.4.3 Community engagement 22 33 26 20 23
IYS.4.4 Better science communication 14 32 28 25 25
IYS.4.5 Traditional ecological knowledge 31 42 25 12 14
IYS.4.6 Young scientists 31 28 35 19 11
IYS.4.7 Changing role of salmon in societies 51 37 16 12 8
IYS.5.1 Database Integration 23 33 34 16 18
IYS.5.2 Knowledge management 25 34 36 16 13
IYS.5.3 Data sharing arrangements 19 37 33 16 19
IYS.5.4 Data visualization 26 35 35 13 15
IYS.6.1 International projects 30 42 30 11 11
IYS.6.2 Celebrating success 28 36 26 20 14
IYS.6.3 Outreach methods, awareness 26 38 28 18 14
IYS.6.4 Engagement FM to science to FM 22 31 31 21 19
IYS.6.5 Linking salmon to climate change 22 47 24 13 18
IYS.1.1 Field Data,20,19,29,23,33
IYS.1.2 Data Analysis,19,29,26,30,20
IYS.1.3 Fishery Management, Assessment,25,23,31,20,25
IYS.1.4 Stock Status Assessment,30,31,27,22,14
IYS.1.5 Habitat Assessment,35,37,22,16,14
IYS.1.6 Population identification,37,25,28,16,18
IYS.1.7 Marine Survival, Growth, Migration,33,16,33,17,25
IYS.1.8 Interactions: Wild, Hatchery, Farmed,36,28,23,14,23
IYS.1.9 Toxicology,67,28,20,5,4
IYS.2.1 Freshwater habitats,29,36,29,16,14
IYS.2.2 Marine and Estuarine Habitats,28,40,28,12,16
IYS.2.3 Climate and Ecosystem Models,33,42,29,11,9
IYS.2.4 Adaptation,32,44,23,13,12
IYS.2.5 Policy and Management,31,45,21,17,10
IYS.3.1 Field methods,21,31,26,23,23
IYS.3.2 Individual fish,36,30,25,20,13
IYS.3.3 Fisheries management process,33,36,23,18,14
IYS.3.4 New analyses,38,37,26,14,9
IYS.3.5 Advances genetics, genomics,46,29,28,12,9
IYS.3.6 Science management,31,30,30,19,14
IYS.3.7 Implementation,22,24,36,27,15
IYS.4.1 First Nations Opportunities,18,30,31,18,27
IYS.4.2 Benefits from Salmon,24,42,26,13,19
IYS.4.3 Community engagement,22,33,26,20,23
IYS.4.4 Better science communication,14,32,28,25,25
IYS.4.5 Traditional ecological knowledge,31,42,25,12,14
IYS.4.6 Young scientists,31,28,35,19,11
IYS.4.7 Changing role of salmon in societies,51,37,16,12,8
IYS.5.1 Database Integration,23,33,34,16,18
IYS.5.2 Knowledge management,25,34,36,16,13
IYS.5.3 Data sharing arrangements,19,37,33,16,19
IYS.5.4 Data visualization,26,35,35,13,15
IYS.6.1 International projects,30,42,30,11,11
IYS.6.2 Celebrating success,28,36,26,20,14
IYS.6.3 Outreach methods, awareness,26,38,28,18,14
IYS.6.4 Engagement FM to science to FM,22,31,31,21,19
IYS.6.5 Linking salmon to climate change,22,47,24,13,18
[1] ""

Pattern of Choices

Without rearranging the IYS topics, the pattern of collaboration choices (1 to 5) is presented. Darker magenta are the most frequent choices, darker cyan are the least frequent.

z=x2[37:1,5:1]  # I have not idea why this flip is required.
IYSplot(z) # uses default text and titles.
Collaboration Choices by IYS Topic, fig.height=6

Collaboration Choices by IYS Topic, fig.height=6

Summary by IYS Theme

From all 37 choices (rows), the count for each of the five choices (columns) were summarized within each of the 6 IYS themes. Because there were varying number of topics (rows) within themes, the average was appropriate rather than the sum. The resulting table with 6 rows is also presented as an image via custom function IYSplot.

# x2 is the 37 by 5 matrix and fctr relates topic to theme 
xtheme=matrix(nrow=6,ncol=5);  # 6 themes by 5 choices
for(j in 1:5) xtheme[,j] = tapply(x2[,j],fctr,mean) # by column
row.names(xtheme) = theme
kable(round(xtheme,0), col.names=1:5, caption="Table x. Collaboration choices by IYS theme from all useful survey responses.")
#print("Choices by Theme, scaled as -1 to +1")
#(((xtheme - min(xtheme)) / (max(xtheme)-min(xtheme) ) -.5)*2) %>% round(1) # -1 to 1.
z=xtheme[6:1,5:1] # again with the freakin' flip. WHT?!
IYSplot(z,ytxt=theme,ylab="IYS Theme")
Figure x. Collaboration choices by IYS theme from all useful survey responses.

Figure x. Collaboration choices by IYS theme from all useful survey responses.

# heatmap(t(xt0), Rowv=NA,Colv=NA)
#cm= xt0 %>% rowMeans; cm; # 5
#xta = xt0 %>% apply(2, function(x) x-cm); xta %>% round(0) %>% print;
Table x. Collaboration choices by IYS theme from all useful survey responses.
1 2 3 4 5
IYS.1 Status Salmon and Habitats 34 26 27 18 20
IYS.2 Effects of Changing Habitats 31 41 26 14 12
IYS.3 New tech and methods 32 31 28 19 14
IYS.4 Connecting Salmon to People 27 35 27 17 18
IYS.5 Information Systems 23 35 34 15 16
IYS.6 Outreach and Communication 26 39 28 17 15

Interpretation

Whilst pondering survey reponses about IYS topics and themes, please note choices within a topic were exclusive. This excluded the ability for a survey responder to plead for help to obtain collaboration for their activities: Choice 2 Yes, but unlikely at present precluded choice 5 This collaboration is vital to my work and should be a priority for DFO. As a result, people who answered the survey humbly mentioned that they were unable to pursue interesting opportunities for collaboration, presumably due to resource constraints (workload, staff, budget), instead of stridently asserting that DFO needs to help them with the collaborations necessary to modernize their activities. Perhaps polite Canadians, perhaps a flaw in the survey design. This exclusion effect applies to IYS Theme 2 Effects of Changing Habitats and to IYS Theme 6 Outreach and Communication (see strong magenta for choice 2 matched with cyan for choice 5 for themes 2 and 6). It’s the faint calls for help that need attention.

With the caveat that topics from different themes were associated with specific collaboration opportunities for survey responders, and that topics within themes might not all be associated that way, this summary of topics by themes is offered:
* Theme 1 (current status of salmon) was voted to be not applicable for collaboration.
* Theme 2 (effects of changing habitats) was strongly interested but cannot pursue
* Theme 3 (new tech and methods) was moderately not applicable and cannot pursue.
* Theme 4 (connecting salmon to people) was clearly interested but cannot pursue.
* Theme 5 (information systems) was a bit more positive, many people were interested and many had projects that needed collaboration re information management and knowledge mobilization.
* Theme 6 (outreach and education), was similar to Theme 2, clearly but less emphatically interested but cannot pursue.

After subtracting the column means, the overall tendency for choices 1 through 5, the preceding summary for Theme 2 was reinforced. This treatment emphasized that Theme 5 (information systems) was something DFO staff wanted for their existing activities.

It is worth noting that the conclusions from votes about collaboration on 37 topics are also the conclusions from summarizing those votes into 6 themes. Restating that conclusion in this context, and guessing at the story behind the numbers: The fact that salmon are facing a changing world (climate change in the Salmosphere) is important to DFO staff, but they need help with data management before they can react. This preliminary and arguable conclusion will be addressed in subsequent analyses of the survey.

Humans also face a rapidly changing world. We need to understand that in order to react wisely, hence the interest in salmon as the canary in the coal mine but on a global scale. If the canaries die (from carbon monixide), then get out of the coal mine, fast. If the salmon die (from global warming), then … ah, nowhere to run.

Scores by Region and Job Classification

A breakdown by region and then job of responses (Complete, useful, not useful) is followed by a similar breakdown after scoring responses. The weights for scoring are: choice 1 (not applicable): 0, choice 2 (deferred): 1, choice 3 (offer activity): 2, choice 4 (offer knowledge): 3, choice 5 (critical, a DFO priority): 4. The intention is to identify the importance of an IYS topic across all the choices.

Score by Importance

The choices for IYS topics are approximately ordinal, from choice 1 which indicates minimal importance to choice 5 which indicates maximal importance. Here the choices by IYS topic were treated as measurements of importance (as rational numbers) supplied by the survey responders, and applied as weights to guage interest in collabortion on specific topics. Setting “not applicable” to a weight of 0 was straightforward. More arbitrary was choosing a quantitative difference to establish the contrast between choice “2: interesting but not immediately applicable” (perhaps for want of resources) and choice “5: vital to my work and should be a high priority for DFO.” Choices with labels “1” to “5” could be given weights 0 to 4 or weights (0 to 4)2 or another weighting. Apart from ranking all of the topics, it was worth noticing topics frequently considered vital and high priority.

Choosing (voting) that a collaboration topic is “critical to my work and should be a high priority” was given 4X the weight of “interesting but unlikely” and the choice “not applicable” did not directly contribute to the scoring, but of course removed a count from another choice.

Results were biased from the initial selection of 361 survey recipients, from self-selection by the 1/3 that responded to at least 25 of the IYS topics, and perhaps subtley from from the order of the IYS themes and topics (although there was no tendancy to skip the last themes). Conversely, people who responded enthusiastically and thoroughly to survey about collaboration potential may be a representative sample (or the population!) of DFO staff who will be epicenters for future collaborations re salmon.

Matrix Multiply

The choices “1” to “5” were weighted 0 to 4, and each row of a matrix of counts of choices (37 rows, 5 cols) is replaced by its weighted sum.

# x0 is 163 by 37, y0 is 163 by 40 with name, region, jobcode.
# x1 has only usefulresponses, 124 by 37. Ditto y1, 124 by 40.
# x2 is the count of choices (1 to 5) by 124 responders, so 37 topics by 5 choices.
# %*% is matrix multipy
x3 <- x2 %*% (0:4) %>% ScaleTo10; 
j1 <- order(x3, decreasing = T)
(data.frame(Topic=topic[j1], Score = x3[j1])) %T>% kable(caption="Table x. Relative Importance of IYS Topics, scored by 124 survey choices, then scaled from 0 to 100");# %T>% Terse 
                                          Topic Score
1                            IYS.1.1 Field Data    10
2           IYS.4.1 First Nations Opportunities     9
3          IYS.4.4 Better science communication     9
4                         IYS.1.2 Data Analysis     8
5        IYS.1.3 Fishery Management, Assessment     8
6                         IYS.3.1 Field methods     8
7                        IYS.3.7 Implementation     8
8                  IYS.4.3 Community engagement     8
9    IYS.1.7 Marine Survival, Growth, Migration     7
10                 IYS.5.1 Database Integration     7
11            IYS.5.3 Data sharing arrangements     7
12       IYS.6.4 Engagement FM to science to FM     7
13              IYS.1.4 Stock Status Assessment     6
14            IYS.1.6 Population identification     6
15 IYS.1.8 Interactions: Wild, Hatchery, Farmed     6
16                  IYS.2.1 Freshwater habitats     6
17                   IYS.3.6 Science management     6
18                 IYS.4.2 Benefits from Salmon     6
19                     IYS.4.6 Young scientists     6
20                 IYS.5.2 Knowledge management     6
21                   IYS.5.4 Data visualization     6
22                  IYS.6.2 Celebrating success     6
23          IYS.6.3 Outreach methods, awareness     6
24     IYS.6.5 Linking salmon to climate change     6
25                   IYS.1.5 Habitat Assessment     5
26        IYS.2.2 Marine and Estuarine Habitats     5
27                      IYS.3.2 Individual fish     5
28         IYS.3.3 Fisheries management process     5
29     IYS.4.5 Traditional ecological knowledge     5
30         IYS.2.3 Climate and Ecosystem Models     4
31                           IYS.2.4 Adaptation     4
32                IYS.2.5 Policy and Management     4
33                         IYS.3.4 New analyses     4
34               IYS.6.1 International projects     4
35          IYS.3.5 Advances genetics, genomics     3
36 IYS.4.7 Changing role of salmon in societies     2
37                           IYS.1.9 Toxicology     0

Score by IYS Theme

The mean within themes of scores for topics.

x3.theme= x3 %>% tapply(fctr,mean) %>% ScaleTo10;
j=order(x3.theme, decreasing = T)
(data.frame(theme[j],x3.theme[j])) %>% kable(col.names=c("Theme", "Score"))
Theme Score
4 IYS.4 Connecting Salmon to People 10
5 IYS.5 Information Systems 10
1 IYS.1 Status Salmon and Habitats 9
6 IYS.6 Outreach and Communication 6
3 IYS.3 New tech and methods 5
2 IYS.2 Effects of Changing Habitats 0

Increased Contrast

To observed the effect of a different weighting, scoring and sorting was repeated with increased contrast, using weights = 0, 1, 4, 9, 16. Choice “5: critical” was thereby 4X more influential than with the previous scoring.

x31 = x2 %*% (0:4)^2 %>% ScaleTo10; 
j2= order(x31,decreasing=TRUE);
data.frame(Topics=topic[j2], Score = x31[j2]) %>% kable;# %T>% Terse;
Topics Score
IYS.1.1 Field Data 10
IYS.1.2 Data Analysis 8
IYS.1.3 Fishery Management, Assessment 8
IYS.3.1 Field methods 8
IYS.4.1 First Nations Opportunities 8
IYS.4.4 Better science communication 8
IYS.1.7 Marine Survival, Growth, Migration 7
IYS.3.7 Implementation 7
IYS.4.3 Community engagement 7
IYS.6.4 Engagement FM to science to FM 7
IYS.1.8 Interactions: Wild, Hatchery, Farmed 6
IYS.5.1 Database Integration 6
IYS.5.3 Data sharing arrangements 6
IYS.1.4 Stock Status Assessment 5
IYS.1.6 Population identification 5
IYS.2.1 Freshwater habitats 5
IYS.2.2 Marine and Estuarine Habitats 5
IYS.3.2 Individual fish 5
IYS.3.3 Fisheries management process 5
IYS.3.6 Science management 5
IYS.4.2 Benefits from Salmon 5
IYS.4.6 Young scientists 5
IYS.5.2 Knowledge management 5
IYS.5.4 Data visualization 5
IYS.6.2 Celebrating success 5
IYS.6.3 Outreach methods, awareness 5
IYS.6.5 Linking salmon to climate change 5
IYS.1.5 Habitat Assessment 4
IYS.4.5 Traditional ecological knowledge 4
IYS.2.3 Climate and Ecosystem Models 3
IYS.2.4 Adaptation 3
IYS.2.5 Policy and Management 3
IYS.3.4 New analyses 3
IYS.3.5 Advances genetics, genomics 3
IYS.6.1 International projects 3
IYS.4.7 Changing role of salmon in societies 2
IYS.1.9 Toxicology 0

The order of topics was not appreciably changed by increase contrast. This suggests the order for choice 5 (vital, high priority) determines the order when choices are scored as a weighted sum. The topics with the top nine scores were identical with and without increased contrast in weights; this true for the bottom nine scores. Note this result was not split by job type.

j1[1:10]; j2[1:10]
(j1[ 1:10] %in% j2[ 1:10]) %>% print
j1[28:37]; j2[28:37]
(j1[28:37] %in% j2[28:38]) %>% print
 [1]  1 22 25  2  3 15 21 24  7 29
 [1]  1  2  3 15 22 25  7 21 24 36
 [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
 [1] 17 26 12 13 14 18 33 19 28  9
 [1]  5 26 12 13 14 18 19 33 28  9
 [1] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE

Scores as a Function

We decided that scores from choices would be important for comparisons of collaboration topics between regions and job-types, and for job-types within Pacific Region.

ScoreTopics = function (a){
    # a is a matrix of counts by topics (row) and choices (col).
    # fctr, topics, theme: inherited
    a1 = a %*% (0:4) %>% ScaleTo10;
    j= order(a1,decreasing=TRUE); 
    data.frame(Topics=topic[j], Score = a1[j]) %T>% 
        kable(col.names=c("Topic", "Score"));
}
ScoreThemes = function (a){
    a1 = a %*% (0:4)
    b.theme= a1 %>% tapply(fctr,mean) %>% ScaleTo10;
    j=order(b.theme,decreasing = T);
    data.frame(Theme=theme[j],Score=b.theme[j]) %>% 
        kable(col.names=c("Theme", "Score")); 
}

Scores by Job

The preceding analysis, weighted sums of choices for IYS topics, is repeated for each of the seven job types. Because there are different numbers of respondents for each job type, they result is scaled 0 to 10 within each job type. This removes the effect of differing sample sizes. The result is presented as single table with job type as columns. To prevent confusion (maybe) the result for all job types (last column) is the mean of the scaled scores for each job type (preceding columns), but not scaled 0 to 10. The effect is to assign equal weight to each job type, after scaling.

# x3 is the score for all 124 responses, vector 37
# x1 is 124 by 37
scoreJob <- matrix(nrow = 37,ncol = 8); 
for(j in 1:7){
    k <- y1$jobCode == jobCode$code[j];  # who has job j
    a <- apply(x1[k,], 2, ChoiceTabSum);  # count of choices, 5 by 37
    scoreJob[,j] <- t(a) %*% (0:4) %>% ScaleTo10;  # score
}
scoreJob[,8] <- scoreJob[,1:7] %>% apply(1,mean) %>% round(0) # previous, score for all 124 responses
dimnames(scoreJob)=list(Topic = topic, Job = c(jobCode$code,"All"))
scoreJob %>% kable; # %T>% Terse;
MA EG RM PO BI RE HA All
IYS.1.1 Field Data 5 10 8 0 9 8 10 7
IYS.1.2 Data Analysis 4 6 7 4 10 6 6 6
IYS.1.3 Fishery Management, Assessment 7 6 10 6 7 1 6 6
IYS.1.4 Stock Status Assessment 7 4 5 2 6 3 8 5
IYS.1.5 Habitat Assessment 3 4 2 6 4 6 10 5
IYS.1.6 Population identification 5 5 5 3 4 5 7 5
IYS.1.7 Marine Survival, Growth, Migration 4 4 5 6 8 10 7 6
IYS.1.8 Interactions: Wild, Hatchery, Farmed 1 5 2 6 5 10 10 6
IYS.1.9 Toxicology 0 0 0 5 0 1 2 1
IYS.2.1 Freshwater habitats 5 4 4 8 6 3 4 5
IYS.2.2 Marine and Estuarine Habitats 4 2 4 4 7 7 5 5
IYS.2.3 Climate and Ecosystem Models 1 2 2 4 6 8 2 4
IYS.2.4 Adaptation 2 3 3 3 6 6 2 4
IYS.2.5 Policy and Management 6 2 5 6 4 2 2 4
IYS.3.1 Field methods 5 8 7 3 7 5 8 6
IYS.3.2 Individual fish 3 7 2 0 6 4 5 4
IYS.3.3 Fisheries management process 6 3 7 6 5 0 2 4
IYS.3.4 New analyses 3 3 2 3 6 4 0 3
IYS.3.5 Advances genetics, genomics 3 2 2 3 3 5 3 3
IYS.3.6 Science management 7 5 3 7 6 5 5 5
IYS.3.7 Implementation 7 6 5 6 8 9 5 7
IYS.4.1 First Nations Opportunities 10 6 9 8 7 4 6 7
IYS.4.2 Benefits from Salmon 8 3 8 5 4 2 5 5
IYS.4.3 Community engagement 7 6 6 10 6 3 9 7
IYS.4.4 Better science communication 9 5 8 8 9 6 7 7
IYS.4.5 Traditional ecological knowledge 9 4 4 5 4 0 4 4
IYS.4.6 Young scientists 7 4 5 8 4 7 4 6
IYS.4.7 Changing role of salmon in societies 3 2 2 3 2 0 1 2
IYS.5.1 Database Integration 4 6 6 5 7 5 5 5
IYS.5.2 Knowledge management 5 5 5 8 6 4 4 5
IYS.5.3 Data sharing arrangements 4 6 6 8 7 4 5 6
IYS.5.4 Data visualization 3 5 3 8 7 5 4 5
IYS.6.1 International projects 6 2 1 9 6 7 1 5
IYS.6.2 Celebrating success 7 4 2 10 5 5 7 6
IYS.6.3 Outreach methods, awareness 6 4 4 8 4 7 8 6
IYS.6.4 Engagement FM to science to FM 8 4 8 8 7 4 4 6
IYS.6.5 Linking salmon to climate change 4 3 4 6 7 6 5 5

Heat Maps by Job Type and Region

We applied a technique for grouping for IYS collaboration topics based on how similar the responses were among topics by people who responded usefully to the survey (a matrix of 37 topics by five choices). The resulting dendrogram shows the hierarchy of groups and is the basis for rearranging the matrix. The frequency of choices in the rearranged matrix is presented as a scale of colours, a heat map, to show the pattern of choices within the resulting groups. This analysis was applied to the survey results partitioned by job types and regions.

Job Type

Because of small sample size, we excluded job type Policy Analyst or Economist with 3 reponders, and DFO region Central and Arctic with 1 responder. Caution might apply to results fro job type Research Scientist (13 responders) and Manager (Staff) (11 responders). The analysis was repeated with choice 1 no,not applicable removed, to emphasize where any collaboration would be valuable, and repeated again after summarizing the 37 choices according to the six IYS themes.

DFO Managers (Staff)

HMjob(y1,"MA","DFO Managers (Staff)","jobCode")

                name    region jobCode
1   Adam Silverstein   Pacific      MA
11        Ann Susnik   Pacific      MA
45        Doug Bliss      Gulf      MA
60        Helen Kerr Maritimes      MA
76       John Holmes   Pacific      MA
79  Jonathan Fershau   Pacific      MA
92       Laura Brown   Pacific      MA
94        Lei Harris Maritimes      MA
137    Roger Wysocki        HQ      MA
138   Ryan Galbraith   Pacific      MA
146     Serge Doucet      Gulf      MA
151      Steve Gotch   Pacific      MA
                                             [,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data                              4    2    2    3    1
IYS.1.2 Data Analysis                           4    2    3    3    0
IYS.1.3 Fishery Management, Assessment          3    2    3    1    3
IYS.1.4 Stock Status Assessment                 2    2    5    2    1
IYS.1.5 Habitat Assessment                      4    5    1    2    0
IYS.1.6 Population identification               3    2    6    0    1
IYS.1.7 Marine Survival, Growth, Migration      5    1    4    0    2
IYS.1.8 Interactions: Wild, Hatchery, Farmed    8    2    0    0    2
IYS.1.9 Toxicology                              8    2    1    1    0
IYS.2.1 Freshwater habitats                     3    4    2    2    1
IYS.2.2 Marine and Estuarine Habitats           4    3    2    2    1
IYS.2.3 Climate and Ecosystem Models            5    5    1    1    0
IYS.2.4 Adaptation                              4    6    1    1    0
IYS.2.5 Policy and Management                   3    2    2    5    0
IYS.3.1 Field methods                           3    2    4    3    0
IYS.3.2 Individual fish                         5    4    0    3    0
IYS.3.3 Fisheries management process            3    3    1    4    1
IYS.3.4 New analyses                            5    1    4    2    0
IYS.3.5 Advances genetics, genomics             4    2    5    1    0
IYS.3.6 Science management                      1    3    4    3    1
IYS.3.7 Implementation                          4    1    2    3    2
IYS.4.1 First Nations Opportunities             2    3    0    1    6
IYS.4.2 Benefits from Salmon                    2    3    1    3    3
IYS.4.3 Community engagement                    4    1    2    3    2
IYS.4.4 Better science communication            2    2    1    5    2
IYS.4.5 Traditional ecological knowledge        2    3    1    2    4
IYS.4.6 Young scientists                        3    1    3    5    0
IYS.4.7 Changing role of salmon in societies    5    3    0    4    0
IYS.5.1 Database Integration                    4    4    1    2    1
IYS.5.2 Knowledge management                    2    4    4    2    0
IYS.5.3 Data sharing arrangements               4    3    2    2    1
IYS.5.4 Data visualization                      4    3    4    0    1
IYS.6.1 International projects                  4    1    3    2    2
IYS.6.2 Celebrating success                     2    2    4    3    1
IYS.6.3 Outreach methods, awareness             2    4    3    2    1
IYS.6.4 Engagement FM to science to FM          3    1    2    3    3
IYS.6.5 Linking salmon to climate change        4    3    2    2    1
                                    Choice
Theme                                 no pending need offer critical
  IYS.1 Status Salmon and Habitats   4.6     2.2  2.8   1.3      1.1
  IYS.2 Effects of Changing Habitats 3.8     4.0  1.6   2.2      0.4
  IYS.3 New tech and methods         3.6     2.3  2.9   2.7      0.6
  IYS.4 Connecting Salmon to People  2.9     2.3  1.1   3.3      2.4
  IYS.5  Information Systems         3.5     3.5  2.8   1.5      0.8
  IYS.6 Outreach and Communication   3.0     2.2  2.8   2.4      1.6

DFO Resource Managers

HMjob(y1,"RM","DFO Resource Managers")

                     name       region jobCode
5            Andrea Goruk      Pacific      RM
12             Art Demsky      Pacific      RM
18             Brad Fanos      Pacific      RM
21      Brittany Jenewein      Pacific      RM
28          Cathy McClean      Pacific      RM
43           Diana McHugh      Pacific      RM
48               Ed Walls      Pacific      RM
50            Erin Porszt      Pacific      RM
52     Frederic Butruille      Pacific      RM
54            Geoff Perry Newfoundland      RM
56            Greg Hornby      Pacific      RM
58          Haakon Hammer      Pacific      RM
59          Heather Braun      Pacific      RM
64             Jim Echols      Pacific      RM
70             Jeff Grout      Pacific      RM
72           Jeremy Smith      Pacific      RM
74  Jody Mackenzie-Grieve      Pacific      RM
78            John Willis      Pacific      RM
93          Lorne Frisson      Pacific      RM
96              Les Clint      Pacific      RM
97          Linda Stevens      Pacific      RM
111         Matt Mortimer      Pacific      RM
112      Matthew Townsend      Pacific      RM
119         Mike Hawkshaw      Pacific      RM
127            Peter Hall      Pacific      RM
128         Peter Katinic      Pacific      RM
130           Reid Schrul      Pacific      RM
134           Rob Brouwer      Pacific      RM
139         Sandra Davies      Pacific      RM
145        Scott Melville      Pacific      RM
161          Vesta Mather      Pacific      RM
163           Wilf Luedke      Pacific      RM
                                             [,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data                              7    3    6    7    9
IYS.1.2 Data Analysis                           6    5    6   11    4
IYS.1.3 Fishery Management, Assessment          2    4    9    8    9
IYS.1.4 Stock Status Assessment                 6    9    8    8    1
IYS.1.5 Habitat Assessment                     12    8    7    5    0
IYS.1.6 Population identification              10    7    3    7    5
IYS.1.7 Marine Survival, Growth, Migration     10    4    8    4    6
IYS.1.8 Interactions: Wild, Hatchery, Farmed   12    9    4    3    4
IYS.1.9 Toxicology                             14    9    7    2    0
IYS.2.1 Freshwater habitats                    10    5    8    6    3
IYS.2.2 Marine and Estuarine Habitats           8    9    8    4    3
IYS.2.3 Climate and Ecosystem Models            9   12    6    3    2
IYS.2.4 Adaptation                              9   10    7    3    3
IYS.2.5 Policy and Management                   5   13    5    4    5
IYS.3.1 Field methods                           6    6    8    6    6
IYS.3.2 Individual fish                        13    5    9    3    2
IYS.3.3 Fisheries management process            5    9    5    5    8
IYS.3.4 New analyses                           11    9    5    5    2
IYS.3.5 Advances genetics, genomics            11    8    7    4    2
IYS.3.6 Science management                     13    5    7    3    4
IYS.3.7 Implementation                          7    5   12    6    2
IYS.4.1 First Nations Opportunities             2    7    9    6    8
IYS.4.2 Benefits from Salmon                    3    8    9    4    8
IYS.4.3 Community engagement                    5    8    8    5    6
IYS.4.4 Better science communication            3    6    8    8    7
IYS.4.5 Traditional ecological knowledge        6   11    9    2    4
IYS.4.6 Young scientists                        8    5   11    6    2
IYS.4.7 Changing role of salmon in societies   11    8    8    3    2
IYS.5.1 Database Integration                    6    7    9    5    5
IYS.5.2 Knowledge management                    8    5   11    3    5
IYS.5.3 Data sharing arrangements               6    6   11    3    6
IYS.5.4 Data visualization                      9    8    9    4    2
IYS.6.1 International projects                 12    9    7    3    1
IYS.6.2 Celebrating success                    11    6   10    4    1
IYS.6.3 Outreach methods, awareness             8    8    8    7    1
IYS.6.4 Engagement FM to science to FM          3    7    8    9    5
IYS.6.5 Linking salmon to climate change        5   12    8    4    3
                                    Choice
Theme                                 no pending need offer critical
  IYS.1 Status Salmon and Habitats   8.8     6.4  6.4   6.1      4.2
  IYS.2 Effects of Changing Habitats 8.2     9.8  6.8   4.0      3.2
  IYS.3 New tech and methods         9.4     6.7  7.6   4.6      3.7
  IYS.4 Connecting Salmon to People  5.4     7.6  8.9   4.9      5.3
  IYS.5  Information Systems         7.2     6.5 10.0   3.8      4.5
  IYS.6 Outreach and Communication   7.8     8.4  8.2   5.4      2.2

DFO Biologists

HMjob(y1,"BI","DFO Biologists")

                 name             region jobCode
4           Alex Levy          Maritimes      BI
10    Ann-Marie Huang            Pacific      BI
14       Athena Ogden            Pacific      BI
22  Bronwyn MacDonald            Pacific      BI
25       Bruce Patten            Pacific      BI
35         Dan Selbie            Pacific      BI
38       David Hardie          Maritimes      BI
55      Gerald Chaput               Gulf      BI
75       Joel Harding            Pacific      BI
82      Karen Dunmall Central and Arctic      BI
83       Keith Clarke       Newfoundland      BI
98  Louise de Mestral          Maritimes      BI
106  Martha Robertson       Newfoundland      BI
107     Marthe Berube             Quebec      BI
109       Mary Thiess            Pacific      BI
116      Michel Biron               Gulf      BI
123    Paige Ackerman            Pacific      BI
124  Patricia Edwards               Gulf      BI
125  Paul Chamberland               Gulf      BI
126        Pedro Nilo             Quebec      BI
129 Philippe Beaulieu            Pacific      BI
131    Richard Bailey            Pacific      BI
132       Rob Houtman            Pacific      BI
141    Sarah Hawkshaw            Pacific      BI
143    Scott Akenhead            Pacific      BI
148   Shelee Hamilton            Pacific      BI
153 Steven Leadbeater          Maritimes      BI
155    Strahan Tucker            Pacific      BI
157         Sue Grant            Pacific      BI
                                             [,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data                              5    6    7    5    6
IYS.1.2 Data Analysis                           2    8    6    6    7
IYS.1.3 Fishery Management, Assessment          9    3   10    4    3
IYS.1.4 Stock Status Assessment                 9    9    4    2    5
IYS.1.5 Habitat Assessment                     11    9    7    0    2
IYS.1.6 Population identification              11    7    7    3    1
IYS.1.7 Marine Survival, Growth, Migration      7    4    9    3    6
IYS.1.8 Interactions: Wild, Hatchery, Farmed    9    8    6    5    1
IYS.1.9 Toxicology                             24    3    1    1    0
IYS.2.1 Freshwater habitats                     6   10    8    3    2
IYS.2.2 Marine and Estuarine Habitats           6    8   10    1    4
IYS.2.3 Climate and Ecosystem Models            7   10    8    2    2
IYS.2.4 Adaptation                              7    9    8    3    2
IYS.2.5 Policy and Management                   7   15    3    3    1
IYS.3.1 Field methods                           8    9    2    4    6
IYS.3.2 Individual fish                        10    7    2    6    4
IYS.3.3 Fisheries management process            6   14    4    4    1
IYS.3.4 New analyses                            7   11    6    3    2
IYS.3.5 Advances genetics, genomics            12   10    4    2    1
IYS.3.6 Science management                      7   10    6    4    2
IYS.3.7 Implementation                          3   11    6    7    2
IYS.4.1 First Nations Opportunities             7    6    8    4    4
IYS.4.2 Benefits from Salmon                    9   12    5    1    2
IYS.4.3 Community engagement                    7    9    6    4    3
IYS.4.4 Better science communication            3    9    7    5    5
IYS.4.5 Traditional ecological knowledge       12    9    4    2    2
IYS.4.6 Young scientists                       13    8    4    2    2
IYS.4.7 Changing role of salmon in societies   16    9    1    1    2
IYS.5.1 Database Integration                    7   10    5    2    5
IYS.5.2 Knowledge management                    9    9    5    3    3
IYS.5.3 Data sharing arrangements               6   11    4    3    5
IYS.5.4 Data visualization                      4   12    5    3    5
IYS.6.1 International projects                  4   15    6    3    1
IYS.6.2 Celebrating success                     7   13    3    4    2
IYS.6.3 Outreach methods, awareness             9   11    6    2    1
IYS.6.4 Engagement FM to science to FM          5    8   10    3    3
IYS.6.5 Linking salmon to climate change        4   15    3    1    6
                                    Choice
Theme                                 no pending need offer critical
  IYS.1 Status Salmon and Habitats   9.7     6.3  6.3   3.2      3.4
  IYS.2 Effects of Changing Habitats 6.6    10.4  7.4   2.4      2.2
  IYS.3 New tech and methods         7.6    10.3  4.3   4.3      2.6
  IYS.4 Connecting Salmon to People  9.6     8.9  5.0   2.7      2.9
  IYS.5  Information Systems         6.5    10.5  4.8   2.8      4.5
  IYS.6 Outreach and Communication   5.8    12.4  5.6   2.6      2.6

DFO Scientists

HMjob(y1,"RE","DFO Scientists")

                      name    region jobCode
27             Carrie Holt   Pacific      RE
30         Chris McKindsey    Quebec      RE
37            David Cairns      Gulf      RE
65              Jim Irvine   Pacific      RE
89               Kim Hyatt   Pacific      RE
91  Kristi Miller-Saunders   Pacific      RE
101            Marc Trudel Maritimes      RE
108      Martin Castonguay    Quebec      RE
115       Michael Scarratt    Quebec      RE
118          Mike Bradford   Pacific      RE
135             Bob Devlin   Pacific      RE
152        Steve MacDonald   Pacific      RE
154        Stewart Johnson   Pacific      RE
                                             [,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data                              2    3    4    2    2
IYS.1.2 Data Analysis                           3    5    0    4    1
IYS.1.3 Fishery Management, Assessment          6    6    0    0    1
IYS.1.4 Stock Status Assessment                 6    4    0    3    0
IYS.1.5 Habitat Assessment                      3    4    3    1    2
IYS.1.6 Population identification               6    2    1    1    3
IYS.1.7 Marine Survival, Growth, Migration      2    2    3    3    3
IYS.1.8 Interactions: Wild, Hatchery, Farmed    1    3    4    2    3
IYS.1.9 Toxicology                              8    3    1    0    1
IYS.2.1 Freshwater habitats                     5    5    1    1    1
IYS.2.2 Marine and Estuarine Habitats           2    6    2    0    3
IYS.2.3 Climate and Ecosystem Models            3    3    3    1    3
IYS.2.4 Adaptation                              4    4    2    0    3
IYS.2.5 Policy and Management                   6    4    2    1    0
IYS.3.1 Field methods                           2    6    2    3    0
IYS.3.2 Individual fish                         3    7    1    1    1
IYS.3.3 Fisheries management process            8    3    2    0    0
IYS.3.4 New analyses                            5    4    2    0    2
IYS.3.5 Advances genetics, genomics             6    3    0    1    3
IYS.3.6 Science management                      2    5    4    2    0
IYS.3.7 Implementation                          1    3    5    2    2
IYS.4.1 First Nations Opportunities             3    7    1    2    0
IYS.4.2 Benefits from Salmon                    4    7    1    1    0
IYS.4.3 Community engagement                    3    7    2    1    0
IYS.4.4 Better science communication            2    4    5    1    1
IYS.4.5 Traditional ecological knowledge        7    4    2    0    0
IYS.4.6 Young scientists                        2    3    5    2    1
IYS.4.7 Changing role of salmon in societies    7    4    2    0    0
IYS.5.1 Database Integration                    5    2    4    1    1
IYS.5.2 Knowledge management                    5    4    2    1    1
IYS.5.3 Data sharing arrangements               3    6    3    0    1
IYS.5.4 Data visualization                      5    3    2    1    2
IYS.6.1 International projects                  2    4    4    1    2
IYS.6.2 Celebrating success                     3    4    3    3    0
IYS.6.3 Outreach methods, awareness             2    5    2    2    2
IYS.6.4 Engagement FM to science to FM          4    5    3    0    1
IYS.6.5 Linking salmon to climate change        3    3    4    2    1
                                    Choice
Theme                                 no pending need offer critical
  IYS.1 Status Salmon and Habitats   4.1     3.6  1.8   1.8      1.8
  IYS.2 Effects of Changing Habitats 4.0     4.4  2.0   0.6      2.0
  IYS.3 New tech and methods         3.9     4.4  2.3   1.3      1.1
  IYS.4 Connecting Salmon to People  4.0     5.1  2.6   1.0      0.3
  IYS.5  Information Systems         4.5     3.8  2.8   0.8      1.2
  IYS.6 Outreach and Communication   2.8     4.2  3.2   1.6      1.2

DFO Enhancement Staff

HMjob(y1, "HA","DFO Enhancement Staff")

                 name    region jobCode
2          Al Jonsson   Pacific      HA
16     Beth Lenentine Maritimes      HA
29    Chantal Nessman   Pacific      HA
34    Dale Desrochers   Pacific      HA
36        Dave Davies   Pacific      HA
44      Don MacKinlay   Pacific      HA
63         James Bell Maritimes      HA
66        James Weger   Pacific      HA
69      Jason Mahoney   Pacific      HA
87         Kerra Shaw   Pacific      HA
113       Mike Goguen Maritimes      HA
133 Robert Beaumaster Maritimes      HA
136      Rob Schaefer   Pacific      HA
142      Sarah Tuziak Maritimes      HA
144    Scott Ducharme   Pacific      HA
                                             [,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data                              0    2    5    2    6
IYS.1.2 Data Analysis                           1    4    4    3    3
IYS.1.3 Fishery Management, Assessment          2    2    5    2    4
IYS.1.4 Stock Status Assessment                 1    2    5    2    5
IYS.1.5 Habitat Assessment                      1    2    1    5    6
IYS.1.6 Population identification               2    2    4    2    5
IYS.1.7 Marine Survival, Growth, Migration      3    1    3    3    5
IYS.1.8 Interactions: Wild, Hatchery, Farmed    2    2    1    1    9
IYS.1.9 Toxicology                              3    5    4    1    2
IYS.2.1 Freshwater habitats                     2    5    3    2    3
IYS.2.2 Marine and Estuarine Habitats           1    6    3    2    3
IYS.2.3 Climate and Ecosystem Models            3    5    4    2    1
IYS.2.4 Adaptation                              2    7    2    2    2
IYS.2.5 Policy and Management                   3    7    2    0    3
IYS.3.1 Field methods                           0    3    6    2    4
IYS.3.2 Individual fish                         2    3    6    1    3
IYS.3.3 Fisheries management process            3    4    5    1    2
IYS.3.4 New analyses                            4    6    3    1    1
IYS.3.5 Advances genetics, genomics             3    3    6    1    2
IYS.3.6 Science management                      3    2    5    2    3
IYS.3.7 Implementation                          3    2    4    2    4
IYS.4.1 First Nations Opportunities             1    3    6    2    3
IYS.4.2 Benefits from Salmon                    1    5    4    2    3
IYS.4.3 Community engagement                    0    3    4    2    6
IYS.4.4 Better science communication            1    5    2    2    5
IYS.4.5 Traditional ecological knowledge        1    5    5    3    1
IYS.4.6 Young scientists                        1    5    6    0    3
IYS.4.7 Changing role of salmon in societies    3    7    2    1    2
IYS.5.1 Database Integration                    1    4    6    0    4
IYS.5.2 Knowledge management                    1    5    6    1    2
IYS.5.3 Data sharing arrangements               0    5    6    1    3
IYS.5.4 Data visualization                      2    4    6    0    3
IYS.6.1 International projects                  2    7    4    1    1
IYS.6.2 Celebrating success                     0    5    3    3    4
IYS.6.3 Outreach methods, awareness             0    4    4    2    5
IYS.6.4 Engagement FM to science to FM          3    3    4    3    2
IYS.6.5 Linking salmon to climate change        1    5    4    2    3
                                    Choice
Theme                                 no pending need offer critical
  IYS.1 Status Salmon and Habitats   1.7     2.4  3.6   2.3      5.0
  IYS.2 Effects of Changing Habitats 2.2     6.0  2.8   1.6      2.4
  IYS.3 New tech and methods         2.6     3.3  5.0   1.4      2.7
  IYS.4 Connecting Salmon to People  1.1     4.7  4.1   1.7      3.3
  IYS.5  Information Systems         1.0     4.5  6.0   0.5      3.0
  IYS.6 Outreach and Communication   1.2     4.8  3.8   2.2      3.0

Interpretation: Collaboration Potential by Job Type

The clearest choices regarding collaboration were:
* disinterest in toxicology and in the role of salmon in societies
* interest but inability to collaborate, on linking salmon to climate change, including adaptation of salmon, and policy and management.
* existing activities need assistance with implmentation of existing/new science, with various aspects of data management including visualization, sharing, integration (IYS theme 5).
* suprisingly, the need collabortion with data managment did not have corresponding offers of knowledge, but there was interest in helping with data analysis and the implementation of science.
* The outstanding choice for as “critical to my work and should be a DFO priority” was field data, followed (all of equal importance) by First Nations opportunties, fisheries management and assessment, marine growth and survival, better science communication, and better field methods.

Tentative Summary

DFO staff have a problem with data collection and management and this is likely blocking offers help with data analysis and improve management technology. Combined interest in stock assessment methodology with marine survival points to the fundamental problem of predictability for fisheries. Could it be that a lot of DFO staff know exactly what needs to be fixed, but cannot marshall the projects/programs necessary to accomplish that fix?

The impression I received from all of the responses to IYS topics was a call for modernization of the year to year business of fisheries managment in DFO. Better tools, sort of: more data more easily, better and more accessible methods for archiving, assembling and applying data. The route to accomplish this reflects the goal of the International Year of the Salmon (paraphrased): radically efficient collaboration across technical staff, biologists, scientists, and fishery managers has produced a quantum leap in the application of new and existing science.

By DFO Regions

Because of small sample sizes, we excluded DFO regions Central and Arctic with 1 responder, National Capital Region with 2, and Newfoundland and Labrador with 3. That leaves 4 regions: Maritimes with 16, Gulf with 7, Quebec with 5, and Pacific with 90.

Maritimes Region

table(y1$region)
# region codes, survey1: Pacific, Maritimes, Quebec, Gulf, Newfoundland, HQ, Central and Arctic.
HMjob(y1, "Maritimes","Maritimes Region","region")


Central and Arctic               Gulf                 HQ 
                 1                  7                  2 
         Maritimes       Newfoundland            Pacific 
                16                  3                 90 
            Quebec 
                 5 
                 name    region jobCode
4           Alex Levy Maritimes      BI
15       Becky Graham Maritimes      EG
16     Beth Lenentine Maritimes      HA
33  Cynthia Hawthorne Maritimes      EG
38       David Hardie Maritimes      BI
60         Helen Kerr Maritimes      MA
63         James Bell Maritimes      HA
94         Lei Harris Maritimes      MA
95     Leroy Anderson Maritimes      EG
98  Louise de Mestral Maritimes      BI
101       Marc Trudel Maritimes      RE
113       Mike Goguen Maritimes      HA
120    Mike Thorburne Maritimes      EG
133 Robert Beaumaster Maritimes      HA
142      Sarah Tuziak Maritimes      HA
153 Steven Leadbeater Maritimes      BI
                                             [,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data                              1    3    4    2    6
IYS.1.2 Data Analysis                           3    5    1    4    3
IYS.1.3 Fishery Management, Assessment          4    2    3    2    5
IYS.1.4 Stock Status Assessment                 4    3    4    2    3
IYS.1.5 Habitat Assessment                      3    5    3    2    3
IYS.1.6 Population identification               2    3    4    3    4
IYS.1.7 Marine Survival, Growth, Migration      1    1    6    2    6
IYS.1.8 Interactions: Wild, Hatchery, Farmed    1    2    4    4    5
IYS.1.9 Toxicology                              7    4    3    1    1
IYS.2.1 Freshwater habitats                     1   10    2    1    2
IYS.2.2 Marine and Estuarine Habitats           1    9    2    1    3
IYS.2.3 Climate and Ecosystem Models            5    6    3    0    2
IYS.2.4 Adaptation                              2    7    3    2    2
IYS.2.5 Policy and Management                   5    6    2    2    1
IYS.3.1 Field methods                           2    1    6    2    5
IYS.3.2 Individual fish                         1    2    6    5    2
IYS.3.3 Fisheries management process            4    3    6    1    2
IYS.3.4 New analyses                            3    7    3    0    3
IYS.3.5 Advances genetics, genomics             3    5    5    0    3
IYS.3.6 Science management                      0    6    7    1    2
IYS.3.7 Implementation                          1    2    7    1    5
IYS.4.1 First Nations Opportunities             2    4    2    4    4
IYS.4.2 Benefits from Salmon                    3    5    3    2    3
IYS.4.3 Community engagement                    2    6    3    2    3
IYS.4.4 Better science communication            2    5    3    3    3
IYS.4.5 Traditional ecological knowledge        5    3    2    4    2
IYS.4.6 Young scientists                        3    5    3    2    3
IYS.4.7 Changing role of salmon in societies    6    4    3    2    1
IYS.5.1 Database Integration                    3    4    5    2    2
IYS.5.2 Knowledge management                    4    5    3    2    2
IYS.5.3 Data sharing arrangements               2    6    3    3    2
IYS.5.4 Data visualization                      4    5    3    2    2
IYS.6.1 International projects                  2    8    3    1    2
IYS.6.2 Celebrating success                     3    4    3    5    1
IYS.6.3 Outreach methods, awareness             1    7    4    3    1
IYS.6.4 Engagement FM to science to FM          2    8    2    3    1
IYS.6.5 Linking salmon to climate change        1    9    2    2    2
                                    Choice
Theme                                 no pending need offer critical
  IYS.1 Status Salmon and Habitats   2.9     3.1  3.6   2.4      4.0
  IYS.2 Effects of Changing Habitats 2.8     7.6  2.4   1.2      2.0
  IYS.3 New tech and methods         2.0     3.7  5.7   1.4      3.1
  IYS.4 Connecting Salmon to People  3.3     4.6  2.7   2.7      2.7
  IYS.5  Information Systems         3.2     5.0  3.5   2.2      2.0
  IYS.6 Outreach and Communication   1.8     7.2  2.8   2.8      1.4

Gulf Region

HMjob(y1, "Gulf","Gulf Region","region" )

                name region jobCode
37      David Cairns   Gulf      RE
45        Doug Bliss   Gulf      MA
55     Gerald Chaput   Gulf      BI
116     Michel Biron   Gulf      BI
124 Patricia Edwards   Gulf      BI
125 Paul Chamberland   Gulf      BI
146     Serge Doucet   Gulf      MA
                                             [,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data                              0    2    2    3    0
IYS.1.2 Data Analysis                           0    2    2    2    1
IYS.1.3 Fishery Management, Assessment          1    2    2    0    2
IYS.1.4 Stock Status Assessment                 1    3    1    0    2
IYS.1.5 Habitat Assessment                      1    4    1    1    0
IYS.1.6 Population identification               2    1    2    1    1
IYS.1.7 Marine Survival, Growth, Migration      2    1    2    1    1
IYS.1.8 Interactions: Wild, Hatchery, Farmed    3    3    0    0    1
IYS.1.9 Toxicology                              5    0    1    1    0
IYS.2.1 Freshwater habitats                     0    3    2    2    0
IYS.2.2 Marine and Estuarine Habitats           1    2    3    1    0
IYS.2.3 Climate and Ecosystem Models            2    1    2    2    0
IYS.2.4 Adaptation                              2    2    1    2    0
IYS.2.5 Policy and Management                   2    3    0    2    0
IYS.3.1 Field methods                           0    2    3    2    0
IYS.3.2 Individual fish                         3    1    1    2    0
IYS.3.3 Fisheries management process            3    2    0    2    0
IYS.3.4 New analyses                            2    2    2    1    0
IYS.3.5 Advances genetics, genomics             2    1    2    2    0
IYS.3.6 Science management                      3    0    1    1    2
IYS.3.7 Implementation                          1    2    1    3    0
IYS.4.1 First Nations Opportunities             1    1    2    0    3
IYS.4.2 Benefits from Salmon                    1    1    2    1    2
IYS.4.3 Community engagement                    1    2    1    1    2
IYS.4.4 Better science communication            1    1    2    0    3
IYS.4.5 Traditional ecological knowledge        2    2    0    1    2
IYS.4.6 Young scientists                        2    2    1    1    1
IYS.4.7 Changing role of salmon in societies    2    2    0    2    1
IYS.5.1 Database Integration                    1    2    3    1    0
IYS.5.2 Knowledge management                    1    2    1    2    1
IYS.5.3 Data sharing arrangements               1    2    4    0    0
IYS.5.4 Data visualization                      1    2    4    0    0
IYS.6.1 International projects                  1    3    0    1    2
IYS.6.2 Celebrating success                     1    3    0    1    2
IYS.6.3 Outreach methods, awareness             1    1    3    1    1
IYS.6.4 Engagement FM to science to FM          1    1    2    1    2
IYS.6.5 Linking salmon to climate change        1    2    2    1    1
                                    Choice
Theme                                 no pending need offer critical
  IYS.1 Status Salmon and Habitats   1.7     2.0  1.4   1.0      0.9
  IYS.2 Effects of Changing Habitats 1.4     2.2  1.6   1.8      0.0
  IYS.3 New tech and methods         2.0     1.4  1.4   1.9      0.3
  IYS.4 Connecting Salmon to People  1.4     1.6  1.1   0.9      2.0
  IYS.5  Information Systems         1.0     2.0  3.0   0.8      0.2
  IYS.6 Outreach and Communication   1.0     2.0  1.4   1.0      1.6

Quebec Region

HMjob(y1, "Quebec","Quebec Region","region")

                 name region jobCode
30    Chris McKindsey Quebec      RE
107     Marthe Berube Quebec      BI
108 Martin Castonguay Quebec      RE
115  Michael Scarratt Quebec      RE
126        Pedro Nilo Quebec      BI
                                             [,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data                              1    1    1    1    1
IYS.1.2 Data Analysis                           1    1    1    2    0
IYS.1.3 Fishery Management, Assessment          3    1    1    0    0
IYS.1.4 Stock Status Assessment                 1    3    1    0    0
IYS.1.5 Habitat Assessment                      1    2    1    0    1
IYS.1.6 Population identification               3    2    0    0    0
IYS.1.7 Marine Survival, Growth, Migration      0    2    1    1    1
IYS.1.8 Interactions: Wild, Hatchery, Farmed    0    3    2    0    0
IYS.1.9 Toxicology                              2    3    0    0    0
IYS.2.1 Freshwater habitats                     3    0    1    0    1
IYS.2.2 Marine and Estuarine Habitats           1    2    1    0    1
IYS.2.3 Climate and Ecosystem Models            2    0    2    0    1
IYS.2.4 Adaptation                              2    1    1    0    1
IYS.2.5 Policy and Management                   2    2    1    0    0
IYS.3.1 Field methods                           1    2    0    1    1
IYS.3.2 Individual fish                         1    3    0    0    1
IYS.3.3 Fisheries management process            2    3    0    0    0
IYS.3.4 New analyses                            3    1    0    1    0
IYS.3.5 Advances genetics, genomics             4    1    0    0    0
IYS.3.6 Science management                      0    4    1    0    0
IYS.3.7 Implementation                          0    3    1    1    0
IYS.4.1 First Nations Opportunities             1    2    1    0    1
IYS.4.2 Benefits from Salmon                    1    3    1    0    0
IYS.4.3 Community engagement                    1    2    1    1    0
IYS.4.4 Better science communication            1    1    2    1    0
IYS.4.5 Traditional ecological knowledge        2    1    1    1    0
IYS.4.6 Young scientists                        2    2    0    1    0
IYS.4.7 Changing role of salmon in societies    2    3    0    0    0
IYS.5.1 Database Integration                    2    1    1    0    1
IYS.5.2 Knowledge management                    3    2    0    0    0
IYS.5.3 Data sharing arrangements               1    2    1    0    1
IYS.5.4 Data visualization                      2    1    1    0    1
IYS.6.1 International projects                  1    2    1    1    0
IYS.6.2 Celebrating success                     2    2    0    1    0
IYS.6.3 Outreach methods, awareness             1    3    0    1    0
IYS.6.4 Engagement FM to science to FM          1    3    1    0    0
IYS.6.5 Linking salmon to climate change        3    2    0    0    0
                                    Choice
Theme                                 no pending need offer critical
  IYS.1 Status Salmon and Habitats   1.3     2.0  0.9   0.4      0.3
  IYS.2 Effects of Changing Habitats 2.0     1.0  1.2   0.0      0.8
  IYS.3 New tech and methods         1.6     2.4  0.3   0.4      0.3
  IYS.4 Connecting Salmon to People  1.4     2.0  0.9   0.6      0.1
  IYS.5  Information Systems         2.0     1.5  0.8   0.0      0.8
  IYS.6 Outreach and Communication   1.6     2.4  0.4   0.6      0.0

Pacific Region

HMjob(y1, "Pacific","Pacific Region","region")

                      name  region jobCode
1         Adam Silverstein Pacific      MA
2               Al Jonsson Pacific      HA
3            Aleta Rushton Pacific      EG
5             Andrea Goruk Pacific      RM
6          Andrew Campbell Pacific      EG
9            Angela Stadel Pacific      PO
10         Ann-Marie Huang Pacific      BI
11              Ann Susnik Pacific      MA
12              Art Demsky Pacific      RM
14            Athena Ogden Pacific      BI
18              Brad Fanos Pacific      RM
20              Brian Leaf Pacific      EG
21       Brittany Jenewein Pacific      RM
22       Bronwyn MacDonald Pacific      BI
24            Bruce Baxter Pacific      EG
25            Bruce Patten Pacific      BI
27             Carrie Holt Pacific      RE
28           Cathy McClean Pacific      RM
29         Chantal Nessman Pacific      HA
32           Colin Nettles Pacific      EG
34         Dale Desrochers Pacific      HA
35              Dan Selbie Pacific      BI
36             Dave Davies Pacific      HA
43            Diana McHugh Pacific      RM
44           Don MacKinlay Pacific      HA
46            Eamon Miyagi Pacific      EG
48                Ed Walls Pacific      RM
49               Elan Park Pacific      PO
50             Erin Porszt Pacific      RM
52      Frederic Butruille Pacific      RM
56             Greg Hornby Pacific      RM
58           Haakon Hammer Pacific      RM
59           Heather Braun Pacific      RM
64              Jim Echols Pacific      RM
65              Jim Irvine Pacific      RE
66             James Weger Pacific      HA
67             Jason Evans Pacific      EG
69           Jason Mahoney Pacific      HA
70              Jeff Grout Pacific      RM
72            Jeremy Smith Pacific      RM
74   Jody Mackenzie-Grieve Pacific      RM
75            Joel Harding Pacific      BI
76             John Holmes Pacific      MA
78             John Willis Pacific      RM
79        Jonathan Fershau Pacific      MA
80          Julia Bradshaw Pacific      EG
87              Kerra Shaw Pacific      HA
89               Kim Hyatt Pacific      RE
91  Kristi Miller-Saunders Pacific      RE
92             Laura Brown Pacific      MA
93           Lorne Frisson Pacific      RM
96               Les Clint Pacific      RM
97           Linda Stevens Pacific      RM
103          Marilyn Helin Pacific      EG
104        Marina Milligan Pacific      EG
109            Mary Thiess Pacific      BI
111          Matt Mortimer Pacific      RM
112       Matthew Townsend Pacific      RM
118          Mike Bradford Pacific      RE
119          Mike Hawkshaw Pacific      RM
122        Nicholas Komick Pacific      EG
123         Paige Ackerman Pacific      BI
127             Peter Hall Pacific      RM
128          Peter Katinic Pacific      RM
129      Philippe Beaulieu Pacific      BI
130            Reid Schrul Pacific      RM
131         Richard Bailey Pacific      BI
132            Rob Houtman Pacific      BI
134            Rob Brouwer Pacific      RM
135             Bob Devlin Pacific      RE
136           Rob Schaefer Pacific      HA
138         Ryan Galbraith Pacific      MA
139          Sandra Davies Pacific      RM
140           Sandy Devcic Pacific      EG
141         Sarah Hawkshaw Pacific      BI
143         Scott Akenhead Pacific      BI
144         Scott Ducharme Pacific      HA
145         Scott Melville Pacific      RM
147          Shaun Spenard Pacific      EG
148        Shelee Hamilton Pacific      BI
150         Stefan Howarth Pacific      EG
151            Steve Gotch Pacific      MA
152        Steve MacDonald Pacific      RE
154        Stewart Johnson Pacific      RE
155         Strahan Tucker Pacific      BI
156          Stuart LePage Pacific      EG
157              Sue Grant Pacific      BI
159             Tracy Cone Pacific      EG
161           Vesta Mather Pacific      RM
163            Wilf Luedke Pacific      RM
                                             [,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data                             17   11   21   16   25
IYS.1.2 Data Analysis                          15   19   19   21   16
IYS.1.3 Fishery Management, Assessment         15   17   24   16   18
IYS.1.4 Stock Status Assessment                22   20   21   18    9
IYS.1.5 Habitat Assessment                     28   22   17   13   10
IYS.1.6 Population identification              28   18   21   12   11
IYS.1.7 Marine Survival, Growth, Migration     28   10   22   13   17
IYS.1.8 Interactions: Wild, Hatchery, Farmed   29   19   17    9   16
IYS.1.9 Toxicology                             47   21   16    3    3
IYS.2.1 Freshwater habitats                    24   21   23   11   11
IYS.2.2 Marine and Estuarine Habitats          24   24   20   10   12
IYS.2.3 Climate and Ecosystem Models           23   30   22    9    6
IYS.2.4 Adaptation                             26   32   16    7    9
IYS.2.5 Policy and Management                  21   32   18   10    9
IYS.3.1 Field methods                          17   24   17   16   16
IYS.3.2 Individual fish                        28   22   18   13    9
IYS.3.3 Fisheries management process           24   26   16   12   12
IYS.3.4 New analyses                           27   26   20   11    6
IYS.3.5 Advances genetics, genomics            35   19   20   10    6
IYS.3.6 Science management                     28   17   21   14   10
IYS.3.7 Implementation                         20   14   26   20   10
IYS.4.1 First Nations Opportunities            13   20   26   13   18
IYS.4.2 Benefits from Salmon                   18   30   19    9   14
IYS.4.3 Community engagement                   17   22   21   13   17
IYS.4.4 Better science communication           10   22   21   19   18
IYS.4.5 Traditional ecological knowledge       20   33   22    6    9
IYS.4.6 Young scientists                       23   17   30   13    7
IYS.4.7 Changing role of salmon in societies   38   25   13    8    6
IYS.5.1 Database Integration                   16   23   24   12   15
IYS.5.2 Knowledge management                   15   23   31   11   10
IYS.5.3 Data sharing arrangements              14   24   24   12   16
IYS.5.4 Data visualization                     18   23   27   10   12
IYS.6.1 International projects                 25   28   25    6    6
IYS.6.2 Celebrating success                    21   26   21   11   11
IYS.6.3 Outreach methods, awareness            22   24   20   12   12
IYS.6.4 Engagement FM to science to FM         16   19   25   14   16
IYS.6.5 Linking salmon to climate change       16   31   19   10   14
                                    Choice
Theme                                  no pending need offer critical
  IYS.1 Status Salmon and Habitats   25.4    17.4 19.8  13.4     13.9
  IYS.2 Effects of Changing Habitats 23.6    27.8 19.8   9.4      9.4
  IYS.3 New tech and methods         25.6    21.1 19.7  13.7      9.9
  IYS.4 Connecting Salmon to People  19.9    24.1 21.7  11.6     12.7
  IYS.5  Information Systems         15.8    23.2 26.5  11.2     13.2
  IYS.6 Outreach and Communication   20.0    25.6 22.0  10.6     11.8

Within in the Pacific Region

Pacific Region of DFO had 90 of the 124 useful responses, allowing a within-region analysis of collaboration choices for IYS topics by job type. The exception is Policy and Economists, with only two responders from Pacific (Angela Stadel, Elan Parl).

Four DFO regions, namely Newfoundland and Labrador, Quebec, Gulf, and Maritimes deal with Atlantic Salmon instead of Pacific Salmon. They had 31 useful responses, including zero for job type PO.

y1p=y1[y1$region == "Pacific",]
y1a=y1[ !(y1$region %in% c("Pacific","Central and Arctic","HQ")) ,] # 31
table(y1p$jobCode) %>% kable(caption = "Pacific", col.names=c("Job Type", "Frequency"))
table(y1a$jobCode) %>% kable(caption = "Atlantic", col.names=c("Job Type", "Frequency"))
Pacific
Job Type Frequency
BI 16
EG 16
HA 10
MA 7
PO 2
RE 8
RM 31
Atlantic
Job Type Frequency
BI 12
EG 4
HA 5
MA 4
RE 5
RM 1
**Table x.* * Frequency of Job Type within Pacific Region, from 90 useful survey responses.

DFO Pacific Managers (Staff)

HMjob(y1p,"MA","DFO Pacific: Managers (Staff)")

                name  region jobCode
1   Adam Silverstein Pacific      MA
11        Ann Susnik Pacific      MA
76       John Holmes Pacific      MA
79  Jonathan Fershau Pacific      MA
92       Laura Brown Pacific      MA
138   Ryan Galbraith Pacific      MA
151      Steve Gotch Pacific      MA
                                             [,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data                              2    1    1    2    1
IYS.1.2 Data Analysis                           3    1    1    2    0
IYS.1.3 Fishery Management, Assessment          2    2    2    0    1
IYS.1.4 Stock Status Assessment                 1    2    3    1    0
IYS.1.5 Habitat Assessment                      2    3    1    1    0
IYS.1.6 Population identification               1    2    4    0    0
IYS.1.7 Marine Survival, Growth, Migration      3    1    2    0    1
IYS.1.8 Interactions: Wild, Hatchery, Farmed    5    1    0    0    1
IYS.1.9 Toxicology                              5    2    0    0    0
IYS.2.1 Freshwater habitats                     2    2    1    1    1
IYS.2.2 Marine and Estuarine Habitats           3    1    1    1    1
IYS.2.3 Climate and Ecosystem Models            3    4    0    0    0
IYS.2.4 Adaptation                              3    4    0    0    0
IYS.2.5 Policy and Management                   2    2    2    1    0
IYS.3.1 Field methods                           2    1    2    2    0
IYS.3.2 Individual fish                         3    2    0    2    0
IYS.3.3 Fisheries management process            2    3    1    1    0
IYS.3.4 New analyses                            4    0    3    0    0
IYS.3.5 Advances genetics, genomics             3    0    4    0    0
IYS.3.6 Science management                      1    3    3    0    0
IYS.3.7 Implementation                          3    1    2    0    1
IYS.4.1 First Nations Opportunities             1    2    0    1    3
IYS.4.2 Benefits from Salmon                    1    2    1    2    1
IYS.4.3 Community engagement                    3    0    2    2    0
IYS.4.4 Better science communication            1    2    1    3    0
IYS.4.5 Traditional ecological knowledge        1    2    1    0    3
IYS.4.6 Young scientists                        2    1    1    3    0
IYS.4.7 Changing role of salmon in societies    4    2    0    1    0
IYS.5.1 Database Integration                    3    2    0    1    1
IYS.5.2 Knowledge management                    1    2    4    0    0
IYS.5.3 Data sharing arrangements               3    1    0    2    1
IYS.5.4 Data visualization                      3    1    2    0    1
IYS.6.1 International projects                  3    0    3    0    1
IYS.6.2 Celebrating success                     1    1    3    2    0
IYS.6.3 Outreach methods, awareness             2    2    1    1    1
IYS.6.4 Engagement FM to science to FM          2    1    2    0    2
IYS.6.5 Linking salmon to climate change        3    1    1    1    1
                                    Choice
Theme                                 no pending need offer critical
  IYS.1 Status Salmon and Habitats   2.7     1.7  1.6   0.7      0.4
  IYS.2 Effects of Changing Habitats 2.6     2.6  0.8   0.6      0.4
  IYS.3 New tech and methods         2.6     1.4  2.1   0.7      0.1
  IYS.4 Connecting Salmon to People  1.9     1.6  0.9   1.7      1.0
  IYS.5  Information Systems         2.5     1.5  1.5   0.8      0.8
  IYS.6 Outreach and Communication   2.2     1.0  2.0   0.8      1.0

DFO Resource Managers

HMjob(y1p,"RM","DFO Pacific: Resource Managers")

                     name  region jobCode
5            Andrea Goruk Pacific      RM
12             Art Demsky Pacific      RM
18             Brad Fanos Pacific      RM
21      Brittany Jenewein Pacific      RM
28          Cathy McClean Pacific      RM
43           Diana McHugh Pacific      RM
48               Ed Walls Pacific      RM
50            Erin Porszt Pacific      RM
52     Frederic Butruille Pacific      RM
56            Greg Hornby Pacific      RM
58          Haakon Hammer Pacific      RM
59          Heather Braun Pacific      RM
64             Jim Echols Pacific      RM
70             Jeff Grout Pacific      RM
72           Jeremy Smith Pacific      RM
74  Jody Mackenzie-Grieve Pacific      RM
78            John Willis Pacific      RM
93          Lorne Frisson Pacific      RM
96              Les Clint Pacific      RM
97          Linda Stevens Pacific      RM
111         Matt Mortimer Pacific      RM
112      Matthew Townsend Pacific      RM
119         Mike Hawkshaw Pacific      RM
127            Peter Hall Pacific      RM
128         Peter Katinic Pacific      RM
130           Reid Schrul Pacific      RM
134           Rob Brouwer Pacific      RM
139         Sandra Davies Pacific      RM
145        Scott Melville Pacific      RM
161          Vesta Mather Pacific      RM
163           Wilf Luedke Pacific      RM
                                             [,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data                              7    2    6    7    9
IYS.1.2 Data Analysis                           6    5    6   10    4
IYS.1.3 Fishery Management, Assessment          2    3    9    8    9
IYS.1.4 Stock Status Assessment                 5    9    8    8    1
IYS.1.5 Habitat Assessment                     11    8    7    5    0
IYS.1.6 Population identification              10    7    3    7    4
IYS.1.7 Marine Survival, Growth, Migration      9    4    8    4    6
IYS.1.8 Interactions: Wild, Hatchery, Farmed   12    9    4    3    3
IYS.1.9 Toxicology                             13    9    7    2    0
IYS.2.1 Freshwater habitats                     9    5    8    6    3
IYS.2.2 Marine and Estuarine Habitats           7    9    8    4    3
IYS.2.3 Climate and Ecosystem Models            8   12    6    3    2
IYS.2.4 Adaptation                              9   10    7    2    3
IYS.2.5 Policy and Management                   4   13    5    4    5
IYS.3.1 Field methods                           5    6    8    6    6
IYS.3.2 Individual fish                        12    5    9    3    2
IYS.3.3 Fisheries management process            5    9    4    5    8
IYS.3.4 New analyses                           10    9    5    5    2
IYS.3.5 Advances genetics, genomics            11    8    6    4    2
IYS.3.6 Science management                     13    4    7    3    4
IYS.3.7 Implementation                          7    5   11    6    2
IYS.4.1 First Nations Opportunities             1    7    9    6    8
IYS.4.2 Benefits from Salmon                    2    8    9    4    8
IYS.4.3 Community engagement                    4    8    8    5    6
IYS.4.4 Better science communication            3    5    8    8    7
IYS.4.5 Traditional ecological knowledge        5   11    9    2    4
IYS.4.6 Young scientists                        7    5   11    6    2
IYS.4.7 Changing role of salmon in societies   10    8    8    3    2
IYS.5.1 Database Integration                    6    7    8    5    5
IYS.5.2 Knowledge management                    8    5   10    3    5
IYS.5.3 Data sharing arrangements               6    6   10    3    6
IYS.5.4 Data visualization                      8    8    9    4    2
IYS.6.1 International projects                 11    9    7    3    1
IYS.6.2 Celebrating success                    10    6   10    4    1
IYS.6.3 Outreach methods, awareness             7    8    8    7    1
IYS.6.4 Engagement FM to science to FM          2    7    8    9    5
IYS.6.5 Linking salmon to climate change        4   12    8    4    3
                                    Choice
Theme                                 no pending need offer critical
  IYS.1 Status Salmon and Habitats   8.3     6.2  6.4   6.0      4.0
  IYS.2 Effects of Changing Habitats 7.4     9.8  6.8   3.8      3.2
  IYS.3 New tech and methods         9.0     6.6  7.1   4.6      3.7
  IYS.4 Connecting Salmon to People  4.6     7.4  8.9   4.9      5.3
  IYS.5  Information Systems         7.0     6.5  9.2   3.8      4.5
  IYS.6 Outreach and Communication   6.8     8.4  8.2   5.4      2.2

DFO Biologists

HMjob(y1p,"BI","DFO Pacific: Biologists")

                 name  region jobCode
10    Ann-Marie Huang Pacific      BI
14       Athena Ogden Pacific      BI
22  Bronwyn MacDonald Pacific      BI
25       Bruce Patten Pacific      BI
35         Dan Selbie Pacific      BI
75       Joel Harding Pacific      BI
109       Mary Thiess Pacific      BI
123    Paige Ackerman Pacific      BI
129 Philippe Beaulieu Pacific      BI
131    Richard Bailey Pacific      BI
132       Rob Houtman Pacific      BI
141    Sarah Hawkshaw Pacific      BI
143    Scott Akenhead Pacific      BI
148   Shelee Hamilton Pacific      BI
155    Strahan Tucker Pacific      BI
157         Sue Grant Pacific      BI
                                             [,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data                              5    4    2    2    3
IYS.1.2 Data Analysis                           1    5    1    4    5
IYS.1.3 Fishery Management, Assessment          4    1    5    4    2
IYS.1.4 Stock Status Assessment                 5    3    2    2    4
IYS.1.5 Habitat Assessment                      8    3    4    0    1
IYS.1.6 Population identification               9    2    4    1    0
IYS.1.7 Marine Survival, Growth, Migration      6    1    3    2    4
IYS.1.8 Interactions: Wild, Hatchery, Farmed    6    4    4    1    1
IYS.1.9 Toxicology                             14    1    1    0    0
IYS.2.1 Freshwater habitats                     6    4    5    0    1
IYS.2.2 Marine and Estuarine Habitats           5    4    4    1    2
IYS.2.3 Climate and Ecosystem Models            5    4    5    1    1
IYS.2.4 Adaptation                              6    6    2    1    1
IYS.2.5 Policy and Management                   4    8    1    2    1
IYS.3.1 Field methods                           7    5    1    1    2
IYS.3.2 Individual fish                         8    4    1    1    2
IYS.3.3 Fisheries management process            3    6    3    3    1
IYS.3.4 New analyses                            3    7    3    2    1
IYS.3.5 Advances genetics, genomics             9    4    2    1    0
IYS.3.6 Science management                      5    4    3    3    1
IYS.3.7 Implementation                          3    4    4    4    1
IYS.4.1 First Nations Opportunities             6    3    5    1    1
IYS.4.2 Benefits from Salmon                    7    8    0    0    1
IYS.4.3 Community engagement                    6    5    3    1    1
IYS.4.4 Better science communication            2    6    3    3    2
IYS.4.5 Traditional ecological knowledge        7    6    3    0    0
IYS.4.6 Young scientists                        9    3    3    0    1
IYS.4.7 Changing role of salmon in societies   10    4    0    1    1
IYS.5.1 Database Integration                    4    5    2    1    4
IYS.5.2 Knowledge management                    3    5    4    2    2
IYS.5.3 Data sharing arrangements               3    5    2    2    4
IYS.5.4 Data visualization                      2    6    2    2    4
IYS.6.1 International projects                  3    9    4    0    0
IYS.6.2 Celebrating success                     6    8    1    0    1
IYS.6.3 Outreach methods, awareness             8    6    2    0    0
IYS.6.4 Engagement FM to science to FM          4    3    6    1    2
IYS.6.5 Linking salmon to climate change        3    8    1    0    4
                                    Choice
Theme                                 no pending need offer critical
  IYS.1 Status Salmon and Habitats   6.4     2.7  2.9   1.8      2.2
  IYS.2 Effects of Changing Habitats 5.2     5.2  3.4   1.0      1.2
  IYS.3 New tech and methods         5.4     4.9  2.4   2.1      1.1
  IYS.4 Connecting Salmon to People  6.7     5.0  2.4   0.9      1.0
  IYS.5  Information Systems         3.0     5.2  2.5   1.8      3.5
  IYS.6 Outreach and Communication   4.8     6.8  2.8   0.2      1.4

DFO Scientists

HMjob(y1p,"RE","DFO Pacific: Scientists")

                      name  region jobCode
27             Carrie Holt Pacific      RE
65              Jim Irvine Pacific      RE
89               Kim Hyatt Pacific      RE
91  Kristi Miller-Saunders Pacific      RE
118          Mike Bradford Pacific      RE
135             Bob Devlin Pacific      RE
152        Steve MacDonald Pacific      RE
154        Stewart Johnson Pacific      RE
                                             [,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data                              1    1    4    1    1
IYS.1.2 Data Analysis                           2    3    0    2    1
IYS.1.3 Fishery Management, Assessment          2    5    0    0    1
IYS.1.4 Stock Status Assessment                 4    1    0    3    0
IYS.1.5 Habitat Assessment                      2    1    2    1    2
IYS.1.6 Population identification               2    2    1    1    2
IYS.1.7 Marine Survival, Growth, Migration      1    1    2    2    2
IYS.1.8 Interactions: Wild, Hatchery, Farmed    0    1    3    2    2
IYS.1.9 Toxicology                              5    1    1    0    1
IYS.2.1 Freshwater habitats                     2    3    1    1    1
IYS.2.2 Marine and Estuarine Habitats           1    3    2    0    2
IYS.2.3 Climate and Ecosystem Models            0    3    2    1    2
IYS.2.4 Adaptation                              1    3    2    0    2
IYS.2.5 Policy and Management                   2    3    2    1    0
IYS.3.1 Field methods                           1    5    0    2    0
IYS.3.2 Individual fish                         1    5    0    1    1
IYS.3.3 Fisheries management process            4    2    2    0    0
IYS.3.4 New analyses                            2    3    2    0    1
IYS.3.5 Advances genetics, genomics             2    3    0    1    2
IYS.3.6 Science management                      1    2    3    2    0
IYS.3.7 Implementation                          0    1    4    2    1
IYS.4.1 First Nations Opportunities             1    4    1    2    0
IYS.4.2 Benefits from Salmon                    2    4    1    1    0
IYS.4.3 Community engagement                    1    4    2    1    0
IYS.4.4 Better science communication            0    2    4    1    1
IYS.4.5 Traditional ecological knowledge        3    3    2    0    0
IYS.4.6 Young scientists                        0    1    5    2    0
IYS.4.7 Changing role of salmon in societies    4    2    2    0    0
IYS.5.1 Database Integration                    2    2    3    1    0
IYS.5.2 Knowledge management                    2    3    2    1    0
IYS.5.3 Data sharing arrangements               2    4    2    0    0
IYS.5.4 Data visualization                      3    2    1    1    1
IYS.6.1 International projects                  1    2    3    1    1
IYS.6.2 Celebrating success                     0    3    3    2    0
IYS.6.3 Outreach methods, awareness             0    3    2    2    1
IYS.6.4 Engagement FM to science to FM          1    3    3    0    1
IYS.6.5 Linking salmon to climate change        0    2    4    2    0
                                    Choice
Theme                                 no pending need offer critical
  IYS.1 Status Salmon and Habitats   2.1     1.8  1.4   1.3      1.3
  IYS.2 Effects of Changing Habitats 1.2     3.0  1.8   0.6      1.4
  IYS.3 New tech and methods         1.6     3.0  1.6   1.1      0.7
  IYS.4 Connecting Salmon to People  1.6     2.9  2.4   1.0      0.1
  IYS.5  Information Systems         2.2     2.8  2.0   0.8      0.2
  IYS.6 Outreach and Communication   0.4     2.6  3.0   1.4      0.6

DFO Enhancement Staff

HMjob(y1p, "HA","DFO Pacific: Enhancement Staff")

               name  region jobCode
2        Al Jonsson Pacific      HA
29  Chantal Nessman Pacific      HA
34  Dale Desrochers Pacific      HA
36      Dave Davies Pacific      HA
44    Don MacKinlay Pacific      HA
66      James Weger Pacific      HA
69    Jason Mahoney Pacific      HA
87       Kerra Shaw Pacific      HA
136    Rob Schaefer Pacific      HA
144  Scott Ducharme Pacific      HA
                                             [,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data                              0    1    4    1    4
IYS.1.2 Data Analysis                           1    2    4    1    2
IYS.1.3 Fishery Management, Assessment          2    0    5    1    2
IYS.1.4 Stock Status Assessment                 1    0    5    1    3
IYS.1.5 Habitat Assessment                      1    1    0    4    4
IYS.1.6 Population identification               2    1    3    1    3
IYS.1.7 Marine Survival, Growth, Migration      3    0    2    2    3
IYS.1.8 Interactions: Wild, Hatchery, Farmed    2    1    0    0    7
IYS.1.9 Toxicology                              3    2    3    1    1
IYS.2.1 Freshwater habitats                     2    1    3    2    2
IYS.2.2 Marine and Estuarine Habitats           1    2    3    2    2
IYS.2.3 Climate and Ecosystem Models            2    2    4    2    0
IYS.2.4 Adaptation                              1    4    2    2    1
IYS.2.5 Policy and Management                   2    4    2    0    2
IYS.3.1 Field methods                           0    2    3    2    3
IYS.3.2 Individual fish                         2    2    3    1    2
IYS.3.3 Fisheries management process            3    3    2    1    1
IYS.3.4 New analyses                            4    3    2    1    0
IYS.3.5 Advances genetics, genomics             2    2    4    1    1
IYS.3.6 Science management                      3    1    2    2    2
IYS.3.7 Implementation                          3    1    1    2    3
IYS.4.1 First Nations Opportunities             1    1    5    1    2
IYS.4.2 Benefits from Salmon                    1    3    3    1    2
IYS.4.3 Community engagement                    0    1    3    1    5
IYS.4.4 Better science communication            1    3    1    1    4
IYS.4.5 Traditional ecological knowledge        1    3    4    2    0
IYS.4.6 Young scientists                        1    2    5    0    2
IYS.4.7 Changing role of salmon in societies    3    4    1    1    1
IYS.5.1 Database Integration                    1    2    4    0    3
IYS.5.2 Knowledge management                    1    3    4    1    1
IYS.5.3 Data sharing arrangements               0    3    4    1    2
IYS.5.4 Data visualization                      2    2    4    0    2
IYS.6.1 International projects                  2    3    4    1    0
IYS.6.2 Celebrating success                     0    2    2    2    4
IYS.6.3 Outreach methods, awareness             0    1    3    1    5
IYS.6.4 Engagement FM to science to FM          3    0    3    2    2
IYS.6.5 Linking salmon to climate change        1    2    3    1    3
                                    Choice
Theme                                 no pending need offer critical
  IYS.1 Status Salmon and Habitats   1.7     0.9  2.9   1.3      3.2
  IYS.2 Effects of Changing Habitats 1.6     2.6  2.8   1.6      1.4
  IYS.3 New tech and methods         2.4     2.0  2.4   1.4      1.7
  IYS.4 Connecting Salmon to People  1.1     2.4  3.1   1.0      2.3
  IYS.5  Information Systems         1.0     2.5  4.0   0.5      2.0
  IYS.6 Outreach and Communication   1.2     1.6  3.0   1.4      2.8

DFO Engineering and Technical Staff

HMjob(y1p, "EG","DFO Pacific: Engineering and Technical Staff")

               name  region jobCode
3     Aleta Rushton Pacific      EG
6   Andrew Campbell Pacific      EG
20       Brian Leaf Pacific      EG
24     Bruce Baxter Pacific      EG
32    Colin Nettles Pacific      EG
46     Eamon Miyagi Pacific      EG
67      Jason Evans Pacific      EG
80   Julia Bradshaw Pacific      EG
103   Marilyn Helin Pacific      EG
104 Marina Milligan Pacific      EG
122 Nicholas Komick Pacific      EG
140    Sandy Devcic Pacific      EG
147   Shaun Spenard Pacific      EG
150  Stefan Howarth Pacific      EG
156   Stuart LePage Pacific      EG
159      Tracy Cone Pacific      EG
                                             [,1] [,2] [,3] [,4] [,5]
IYS.1.1 Field Data                              0    2    4    3    7
IYS.1.2 Data Analysis                           1    3    7    2    3
IYS.1.3 Fishery Management, Assessment          2    6    3    3    2
IYS.1.4 Stock Status Assessment                 4    5    3    3    1
IYS.1.5 Habitat Assessment                      4    6    2    2    2
IYS.1.6 Population identification               3    4    6    2    1
IYS.1.7 Marine Survival, Growth, Migration      6    3    4    3    0
IYS.1.8 Interactions: Wild, Hatchery, Farmed    4    3    5    3    1
IYS.1.9 Toxicology                              7    6    3    0    0
IYS.2.1 Freshwater habitats                     3    6    4    1    2
IYS.2.2 Marine and Estuarine Habitats           6    5    2    2    1
IYS.2.3 Climate and Ecosystem Models            4    5    5    2    0
IYS.2.4 Adaptation                              5    4    3    2    2
IYS.2.5 Policy and Management                   6    2    6    2    0
IYS.3.1 Field methods                           1    4    3    3    5
IYS.3.2 Individual fish                         1    3    5    5    2
IYS.3.3 Fisheries management process            6    3    4    2    1
IYS.3.4 New analyses                            3    4    5    3    1
IYS.3.5 Advances genetics, genomics             7    2    4    3    0
IYS.3.6 Science management                      5    2    3    4    2
IYS.3.7 Implementation                          3    2    4    6    1
IYS.4.1 First Nations Opportunities             3    3    6    2    2
IYS.4.2 Benefits from Salmon                    5    4    5    1    1
IYS.4.3 Community engagement                    3    4    3    3    3
IYS.4.4 Better science communication            3    4    4    3    2
IYS.4.5 Traditional ecological knowledge        3    7    3    2    1
IYS.4.6 Young scientists                        4    5    4    2    1
IYS.4.7 Changing role of salmon in societies    6    5    2    2    1
IYS.5.1 Database Integration                    0    4    6    4    2
IYS.5.2 Knowledge management                    0    5    6    4    1
IYS.5.3 Data sharing arrangements               0    5    5    4    2
IYS.5.4 Data visualization                      0    4    8    3    1
IYS.6.1 International projects                  5    5    3    1    2
IYS.6.2 Celebrating success                     4    6    2    1    3
IYS.6.3 Outreach methods, awareness             5    4    3    1    3
IYS.6.4 Engagement FM to science to FM          4    5    2    2    3
IYS.6.5 Linking salmon to climate change        5    6    1    2    2
                                    Choice
Theme                                 no pending need offer critical
  IYS.1 Status Salmon and Habitats   3.4     4.2  4.1   2.3      1.9
  IYS.2 Effects of Changing Habitats 4.8     4.4  4.0   1.8      1.0
  IYS.3 New tech and methods         3.7     2.9  4.0   3.7      1.7
  IYS.4 Connecting Salmon to People  3.9     4.6  3.9   2.1      1.6
  IYS.5  Information Systems         0.0     4.5  6.2   3.8      1.5
  IYS.6 Outreach and Communication   4.6     5.2  2.2   1.4      2.6

Scores by Job: Comparing the Atlantic Salmon Regions to the Pacific Region

As previously for DFO Canada-wide: weighted sums of choices for IYS topics, for each job type. It is necessary to correct the scores for differing numbers of people in each job type (MA, EG, RM, PO, BI, RE, HA).

The jobType PO was not included because it was insufficiently represented (2 responses). This same thing applies to RM for Atlantic (1 response from 4 regions), so that job type was not included in the Atlantic results.

Add a column that is the mean for “ALL” job types. This is not a weighted mean, the rationale is that each job type is equally important to DFO (boat don’t float without techs and scientists and managers).

# x3 is the previous score for all 124 responses, vector 37
# x1 is 124 by 37
# y1p is Pacific region only,  90 by 40, with job,region, name
# y1a is fourAtlantic regions, 31 by 40, with job,region, name
jc=jobCode$code[-4] # delete PO
PacCountJob <- numeric(6); names(PacCountJob) <- jc; AtlCountJob <- PacCountJob 
PacScoreJob <- matrix(NA,nrow=37,ncol=7,dimnames=list(Topic=topic, Job=c(jc,"All"))); AtlScoreJob <- PacScoreJob
# Pacific
for(j in 1:6){
    hasJob <-  y1p$jobCode == jc[j] # who has job
    PacCountJob[j] <- sum(hasJob)
    a <- y1p[hasJob,1:37] %>% apply(2,ChoiceTabSum); # count choices, 5 by 37
    PacScoreJob[,j] <- t(a) %*% (0:4) # weighted sum of choices
    PacScoreJob[,j] <-PacScoreJob[,j]/PacCountJob[j] # correct for sample size
}
PacScoreJob[,7] <- apply(PacScoreJob[,1:6],1,mean)
print(round(PacScoreJob,1))
cat("\nCount of Pacific job types",PacCountJob, "\nsum =",
    sum(PacCountJob),"\n")
# Atlantic 
for(j in 1:6){
    hasJob <-  y1a$jobCode == jc[j] # who has job
    AtlCountJob[j] <- sum(hasJob)
    a <- y1a[hasJob,1:37] %>% apply(2,ChoiceTabSum); # count choices, 5 by 37
    AtlScoreJob[,j] <- t(a) %*% (0:4) # weighted sum of choices
    AtlScoreJob[,j] <-AtlScoreJob[,j]/AtlCountJob[j] # correct for sample size
}
AtlScoreJob[,7] <- apply(AtlScoreJob[,c(1,2, 4,5,6)],1,mean) # mean by of job scores
print(round(AtlScoreJob[,c(1,2, 4,5,6,7)],1))
cat("\nCount of Atlantic job types",AtlCountJob, "\nsum =", sum(AtlCountJob),"\n")
                                              Job
Topic                                           MA  EG  RM  BI  RE  HA All
  IYS.1.1 Field Data                           1.9 2.9 2.3 1.6 2.0 2.8 2.3
  IYS.1.2 Data Analysis                        1.3 2.2 2.0 2.4 1.6 2.1 1.9
  IYS.1.3 Fishery Management, Assessment       1.4 1.8 2.6 1.9 1.1 2.1 1.8
  IYS.1.4 Stock Status Assessment              1.6 1.5 1.7 1.8 1.2 2.5 1.7
  IYS.1.5 Habitat Assessment                   1.1 1.5 1.2 0.9 2.0 2.9 1.6
  IYS.1.6 Population identification            1.4 1.6 1.6 0.8 1.9 2.2 1.6
  IYS.1.7 Marine Survival, Growth, Migration   1.3 1.2 1.8 1.8 2.4 2.2 1.8
  IYS.1.8 Interactions: Wild, Hatchery, Farmed 0.7 1.6 1.2 1.2 2.6 2.9 1.7
  IYS.1.9 Toxicology                           0.3 0.8 0.9 0.2 0.9 1.5 0.8
  IYS.2.1 Freshwater habitats                  1.6 1.6 1.6 1.1 1.5 2.1 1.6
  IYS.2.2 Marine and Estuarine Habitats        1.4 1.2 1.6 1.4 1.9 2.2 1.6
  IYS.2.3 Climate and Ecosystem Models         0.6 1.3 1.3 1.3 2.2 1.6 1.4
  IYS.2.4 Adaptation                           0.6 1.5 1.4 1.1 1.9 1.8 1.4
  IYS.2.5 Policy and Management                1.3 1.2 1.8 1.2 1.2 1.6 1.4
  IYS.3.1 Field methods                        1.6 2.4 2.1 1.1 1.4 2.6 1.9
  IYS.3.2 Individual fish                      1.1 2.2 1.3 1.1 1.5 1.9 1.5
  IYS.3.3 Fisheries management process         1.1 1.3 2.1 1.6 0.8 1.4 1.4
  IYS.3.4 New analyses                         0.9 1.7 1.4 1.4 1.4 1.0 1.3
  IYS.3.5 Advances genetics, genomics          1.1 1.2 1.3 0.7 1.8 1.7 1.3
  IYS.3.6 Science management                   1.3 1.8 1.4 1.4 1.8 1.9 1.6
  IYS.3.7 Implementation                       1.3 2.0 1.7 1.8 2.4 2.1 1.9
  IYS.4.1 First Nations Opportunities          2.4 1.8 2.4 1.2 1.5 2.2 1.9
  IYS.4.2 Benefits from Salmon                 2.0 1.3 2.3 0.8 1.1 2.0 1.6
  IYS.4.3 Community engagement                 1.4 1.9 2.0 1.1 1.4 3.0 1.8
  IYS.4.4 Better science communication         1.9 1.8 2.4 1.8 2.1 2.4 2.1
  IYS.4.5 Traditional ecological knowledge     2.3 1.4 1.6 0.8 0.9 1.7 1.4
  IYS.4.6 Young scientists                     1.7 1.4 1.7 0.8 2.1 2.0 1.6
  IYS.4.7 Changing role of salmon in societies 0.7 1.2 1.3 0.7 0.8 1.3 1.0
  IYS.5.1 Database Integration                 1.3 2.2 1.9 1.8 1.4 2.2 1.8
  IYS.5.2 Knowledge management                 1.4 2.1 1.7 1.7 1.2 1.8 1.7
  IYS.5.3 Data sharing arrangements            1.6 2.2 1.9 1.9 1.0 2.2 1.8
  IYS.5.4 Data visualization                   1.3 2.1 1.5 2.0 1.4 1.8 1.7
  IYS.6.1 International projects               1.4 1.4 1.2 1.1 1.9 1.4 1.4
  IYS.6.2 Celebrating success                  1.9 1.6 1.4 0.9 1.9 2.8 1.7
  IYS.6.3 Outreach methods, awareness          1.6 1.6 1.6 0.6 2.1 3.0 1.7
  IYS.6.4 Engagement FM to science to FM       1.9 1.7 2.3 1.6 1.6 2.0 1.8
  IYS.6.5 Linking salmon to climate change     1.4 1.4 1.7 1.6 2.0 2.3 1.7

Count of Pacific job types 7 16 31 16 8 10 
sum = 88 
                                              Job
Topic                                           MA  EG  BI  RE  HA All
  IYS.1.1 Field Data                           1.5 3.2 2.6 1.8 2.8 2.4
  IYS.1.2 Data Analysis                        1.5 2.0 2.1 1.6 2.4 1.9
  IYS.1.3 Fishery Management, Assessment       2.5 3.2 1.3 0.2 2.6 2.0
  IYS.1.4 Stock Status Assessment              2.0 2.8 1.1 0.6 2.6 1.8
  IYS.1.5 Habitat Assessment                   1.2 2.2 1.2 1.0 2.8 1.7
  IYS.1.6 Population identification            2.0 2.5 1.6 0.8 2.8 1.9
  IYS.1.7 Marine Survival, Growth, Migration   2.0 3.2 2.0 2.0 2.8 2.4
  IYS.1.8 Interactions: Wild, Hatchery, Farmed 1.2 3.0 1.7 1.6 2.8 2.1
  IYS.1.9 Toxicology                           1.2 1.0 0.4 0.4 1.8 1.0
  IYS.2.1 Freshwater habitats                  1.5 2.2 2.0 0.4 1.6 1.6
  IYS.2.2 Marine and Estuarine Habitats        1.5 1.8 1.8 1.4 1.6 1.6
  IYS.2.3 Climate and Ecosystem Models         1.2 1.2 1.5 1.2 1.4 1.3
  IYS.2.4 Adaptation                           1.5 1.5 1.9 1.0 1.4 1.5
  IYS.2.5 Policy and Management                2.2 1.8 1.1 0.2 1.4 1.3
  IYS.3.1 Field methods                        1.8 3.2 2.5 1.6 2.2 2.3
  IYS.3.2 Individual fish                      1.0 2.8 2.2 0.8 2.2 1.8
  IYS.3.3 Fisheries management process         2.5 1.8 1.0 0.2 2.2 1.5
  IYS.3.4 New analyses                         1.5 1.0 1.3 1.0 1.8 1.3
  IYS.3.5 Advances genetics, genomics          1.5 1.2 1.3 0.8 1.8 1.3
  IYS.3.6 Science management                   3.0 2.0 1.5 1.0 2.2 1.9
  IYS.3.7 Implementation                       2.5 2.5 1.9 1.6 2.2 2.1
  IYS.4.1 First Nations Opportunities          3.0 3.2 2.4 0.6 2.2 2.3
  IYS.4.2 Benefits from Salmon                 2.8 2.5 1.7 0.6 2.2 1.9
  IYS.4.3 Community engagement                 2.8 2.5 2.0 0.6 2.2 2.0
  IYS.4.4 Better science communication         2.8 2.5 2.3 0.8 2.2 2.1
  IYS.4.5 Traditional ecological knowledge     2.5 2.5 1.6 0.2 2.2 1.8
  IYS.4.6 Young scientists                     2.0 2.5 1.3 1.2 1.8 1.8
  IYS.4.7 Changing role of salmon in societies 2.2 1.5 0.8 0.4 1.8 1.4
  IYS.5.1 Database Integration                 1.5 2.0 1.5 1.2 2.0 1.6
  IYS.5.2 Knowledge management                 1.8 1.8 1.1 1.0 2.0 1.5
  IYS.5.3 Data sharing arrangements            1.2 2.2 1.4 1.6 2.0 1.7
  IYS.5.4 Data visualization                   1.2 1.0 1.5 1.4 2.0 1.4
  IYS.6.1 International projects               2.0 1.2 1.8 1.6 1.6 1.7
  IYS.6.2 Celebrating success                  2.2 2.2 1.9 0.8 1.6 1.8
  IYS.6.3 Outreach methods, awareness          2.0 1.8 1.8 1.2 1.6 1.7
  IYS.6.4 Engagement FM to science to FM       2.5 2.0 1.9 0.4 1.6 1.7
  IYS.6.5 Linking salmon to climate change     1.5 2.0 1.5 1.0 1.6 1.5

Count of Atlantic job types 4 4 1 12 5 5 
sum = 31 
PacThemeScoreJob <-matrix(NA,6,7,dimnames=list(Theme=theme,JobCode=c(jc,"ALL")))
AtlThemeScoreJob <- PacThemeScoreJob
# Pacific
for(j in 1:6) PacThemeScoreJob[,j] = tapply(PacScoreJob[,j],fctr,mean)
PacThemeScoreJob[,7]= apply(PacThemeScoreJob[,1:6],1, mean)
round(PacThemeScoreJob,1)
# replace with scaled version. Note ALL is mean AFTER scaling, mean of scaled.
PacThemeScoreJob[,1:6] <- apply(PacThemeScoreJob[,1:6], 2, ScaleTo10)
PacThemeScoreJob[,7] <-  apply(PacThemeScoreJob[,1:6], 1, mean) %>% round(0)
PacThemeScoreJob %>% kable(caption="Pacific")
# Atlantic
for(j in 1:6) AtlThemeScoreJob[,j] = tapply(AtlScoreJob[,j],fctr,mean)
AtlThemeScoreJob[,7] <- apply(AtlThemeScoreJob[,c(1,2,4:6)],1, mean) # not RM
round(AtlThemeScoreJob[,c(1,2,4:7)],1)
AtlThemeScoreJob <- apply(AtlThemeScoreJob, 2, ScaleTo10)
AtlThemeScoreJob[,7]  <-  apply(AtlThemeScoreJob[,c(1,2, 4,5,6)], 1, mean) %>% round(0) # not RM
AtlThemeScoreJob[,c(1,2, 4:7)] %>% kable(caption="Atlantic")
                                    JobCode
Theme                                 MA  EG  RM  BI  RE  HA ALL
  IYS.1 Status Salmon and Habitats   1.2 1.7 1.7 1.4 1.8 2.4 1.7
  IYS.2 Effects of Changing Habitats 1.1 1.4 1.5 1.2 1.8 1.9 1.5
  IYS.3 New tech and methods         1.2 1.8 1.6 1.3 1.6 1.8 1.5
  IYS.4 Connecting Salmon to People  1.8 1.6 2.0 1.0 1.4 2.1 1.6
  IYS.5  Information Systems         1.4 2.1 1.8 1.8 1.2 2.0 1.7
  IYS.6 Outreach and Communication   1.6 1.5 1.6 1.2 1.9 2.3 1.7
Pacific
MA EG RM BI RE HA ALL
IYS.1 Status Salmon and Habitats 2 4 4 5 8 10 6
IYS.2 Effects of Changing Habitats 0 0 0 3 8 1 2
IYS.3 New tech and methods 2 6 1 3 5 0 3
IYS.4 Connecting Salmon to People 10 3 10 0 2 5 5
IYS.5 Information Systems 4 10 5 10 0 4 6
IYS.6 Outreach and Communication 8 2 2 2 10 9 6
                                    JobCode
Theme                                 MA  EG  BI  RE  HA ALL
  IYS.1 Status Salmon and Habitats   1.7 2.6 1.6 1.1 2.6 1.9
  IYS.2 Effects of Changing Habitats 1.6 1.7 1.7 0.8 1.5 1.5
  IYS.3 New tech and methods         2.0 2.1 1.7 1.0 2.1 1.8
  IYS.4 Connecting Salmon to People  2.6 2.5 1.7 0.6 2.1 1.9
  IYS.5  Information Systems         1.4 1.8 1.4 1.3 2.0 1.6
  IYS.6 Outreach and Communication   2.0 1.8 1.8 1.0 1.6 1.7
Atlantic
MA EG BI RE HA ALL
IYS.1 Status Salmon and Habitats 2 10 4 7 10 7
IYS.2 Effects of Changing Habitats 1 0 7 3 0 2
IYS.3 New tech and methods 5 4 7 6 5 5
IYS.4 Connecting Salmon to People 10 9 9 0 5 7
IYS.5 Information Systems 0 1 0 10 5 3
IYS.6 Outreach and Communication 5 2 10 6 1 5

Topic Scaling

For the Atlantic and Pacific groups,job scores by topics were scaled 1 to 10 (not ranked). This had to be done after aggregating to themes.

PSJS <- apply(PacScoreJob, 2, ScaleTo10) # within columns.
PSJS[,7] <- PSJS[,1:6] %>% apply(1,mean) %>% round(0)
PSJS[order(PSJS[,7], decreasing=TRUE),] %>% kable # sort by overall interest
# Atlanic
ASJS <- apply(AtlScoreJob, 2, ScaleTo10) # within columns.
ASJS[,7] <- ASJS[,c(1,2,4:6)] %>% apply(1,mean) %>% round(0)
ASJS[order(ASJS[,7], decreasing=TRUE), c(1,2,4:7) ] %>% kable # sort by overall interest
MA EG RM BI RE HA All
IYS.1.1 Field Data 7 10 8 6 7 9 8
IYS.1.2 Data Analysis 5 7 7 10 5 6 7
IYS.4.4 Better science communication 7 5 8 7 7 7 7
IYS.1.3 Fishery Management, Assessment 5 5 10 8 2 6 6
IYS.1.7 Marine Survival, Growth, Migration 5 2 5 7 9 6 6
IYS.3.1 Field methods 6 8 7 4 3 8 6
IYS.3.7 Implementation 5 6 5 7 9 6 6
IYS.4.1 First Nations Opportunities 10 5 9 5 4 6 6
IYS.4.3 Community engagement 5 5 7 4 3 10 6
IYS.5.1 Database Integration 5 7 6 7 3 6 6
IYS.5.3 Data sharing arrangements 6 7 6 8 1 6 6
IYS.6.3 Outreach methods, awareness 6 4 4 2 7 10 6
IYS.6.4 Engagement FM to science to FM 7 4 8 6 5 5 6
IYS.1.4 Stock Status Assessment 6 3 5 7 3 8 5
IYS.1.5 Habitat Assessment 4 3 2 3 7 10 5
IYS.1.6 Population identification 5 4 4 3 6 6 5
IYS.1.8 Interactions: Wild, Hatchery, Farmed 2 4 2 4 10 10 5
IYS.2.1 Freshwater habitats 6 4 4 4 4 6 5
IYS.2.2 Marine and Estuarine Habitats 5 2 4 6 6 6 5
IYS.3.6 Science management 5 5 3 6 5 4 5
IYS.4.2 Benefits from Salmon 8 3 8 2 2 5 5
IYS.4.6 Young scientists 7 3 5 3 7 5 5
IYS.5.2 Knowledge management 5 6 5 7 3 4 5
IYS.5.4 Data visualization 5 6 3 8 3 4 5
IYS.6.2 Celebrating success 7 4 2 3 6 9 5
IYS.6.5 Linking salmon to climate change 5 3 4 6 7 6 5
IYS.2.3 Climate and Ecosystem Models 1 3 2 5 8 3 4
IYS.2.5 Policy and Management 5 2 5 5 3 3 4
IYS.3.2 Individual fish 4 7 2 4 4 4 4
IYS.3.3 Fisheries management process 4 3 7 6 0 2 4
IYS.4.5 Traditional ecological knowledge 9 3 4 2 1 4 4
IYS.6.1 International projects 5 3 1 4 6 2 4
IYS.2.4 Adaptation 1 3 2 4 6 4 3
IYS.3.4 New analyses 3 4 2 6 3 0 3
IYS.3.5 Advances genetics, genomics 4 2 2 2 5 4 3
IYS.4.7 Changing role of salmon in societies 2 2 2 2 0 2 2
IYS.1.9 Toxicology 0 0 0 0 1 2 0
MA EG BI RE HA All
IYS.1.1 Field Data 2 10 10 9 10 8
IYS.1.7 Marine Survival, Growth, Migration 5 10 7 10 10 8
IYS.3.1 Field methods 4 10 10 8 6 8
IYS.1.8 Interactions: Wild, Hatchery, Farmed 1 9 6 8 10 7
IYS.3.7 Implementation 8 7 7 8 6 7
IYS.4.1 First Nations Opportunities 10 10 9 2 6 7
IYS.4.4 Better science communication 9 7 9 3 6 7
IYS.1.2 Data Analysis 2 4 8 8 7 6
IYS.1.3 Fishery Management, Assessment 8 10 4 0 9 6
IYS.1.6 Population identification 5 7 5 3 10 6
IYS.3.6 Science management 10 4 5 4 6 6
IYS.4.2 Benefits from Salmon 9 7 6 2 6 6
IYS.4.3 Community engagement 9 7 7 2 6 6
IYS.1.4 Stock Status Assessment 5 8 3 2 9 5
IYS.1.5 Habitat Assessment 1 6 4 4 10 5
IYS.3.2 Individual fish 0 8 8 3 6 5
IYS.4.5 Traditional ecological knowledge 8 7 5 0 6 5
IYS.4.6 Young scientists 5 7 4 6 3 5
IYS.5.3 Data sharing arrangements 1 6 5 8 4 5
IYS.6.2 Celebrating success 6 6 7 3 1 5
IYS.2.2 Marine and Estuarine Habitats 2 3 7 7 1 4
IYS.3.3 Fisheries management process 8 3 3 0 6 4
IYS.5.1 Database Integration 2 4 5 6 4 4
IYS.5.2 Knowledge management 4 3 3 4 4 4
IYS.6.1 International projects 5 1 7 8 1 4
IYS.6.3 Outreach methods, awareness 5 3 7 6 1 4
IYS.6.4 Engagement FM to science to FM 8 4 7 1 1 4
IYS.2.1 Freshwater habitats 2 6 7 1 1 3
IYS.2.3 Climate and Ecosystem Models 1 1 5 6 0 3
IYS.2.4 Adaptation 2 2 7 4 0 3
IYS.3.4 New analyses 2 0 4 4 3 3
IYS.3.5 Advances genetics, genomics 2 1 4 3 3 3
IYS.4.7 Changing role of salmon in societies 6 2 2 1 3 3
IYS.5.4 Data visualization 1 0 5 7 4 3
IYS.6.5 Linking salmon to climate change 2 4 5 4 1 3
IYS.2.5 Policy and Management 6 3 3 0 0 2
IYS.1.9 Toxicology 1 0 0 1 3 1

Top Topics Plot

The scores for IYS topics reflect interest in particpating in collaboration, something that cannot be entirely separated from personal (non-colaborative) interest or perceived future value regarding an IYS topic. To compare and contrast this interest among job types, we asked: For topics with the highest scores for one or more job types (within Pacific Region), how does that topic score for other job types? Examination of the scaled PacScoreJobs produced 16 such topics. We observed a preponderance of topics from IYS themes 1 (current status of salmon) and 4 (connecting salmon to people), in contrast to themes 2 (future), 3 (new tech), and 5 (information systems). The top interests in collaboration for each job type in Pacific were applied to Atlantic fo comparison. OLD 1.1,1.2,1.3,1.5,1.7,1.8,2.3,3.1,3.7,4.1,4.2,4.3, 4.5,5.3, 6.3,6.4 NEW 1.1,1.2,1.3,1.5,1.7,1.8,2.3,3.1,3.7,4.1, 4.3,4.4,4.5,5.3,6.2,6.3

Pacific Region

# (1.1,1.2,1.3,1.5,1.7,1.8,2.3,3.1,3.7,4.1, 4.2,4.3,4.5,5.3,6.3,6.4) # 16
tops <- as.character(c(1.1,1.2,1.3,1.5,1.7,1.8,2.3,3.1,3.7,4.1,    4.3,4.4,4.5,5.3,6.2,6.3))
nt <- length(tops)
top  <-  which(substring(topic,5,7) %in% tops) 
# 1  2  3  5  7  8 12 15 21 22 24 25 26 31 34 35 which of 37 topics
jc=    jobCode[-4,1] # 6 without PO
mainText=substring(topic[top],9) %T>% print # from nineth character on
SetPar();par(xaxs="r");
par(mfcol=c(4,4), mar=c(1,1,1,0) );
for(j in 1:nt){
    y=PSJS[top[j],1:6]  # how job types responded to a topic
    plot(1:6,y, xlim=c(1,6),ylim=c(-1,11),xaxt="n",yaxt="n",
    xlab="", ylab="",main=mainText[j], cex.main=0.8);
    m=mean(y);    abline(h=m) # a horizontal line at the mean
    segments(1:6, rep(m,6),1:6,y) # connect dot to horizontal line
    axis(3,labels=F); axis(4,at=0:9,labels=F)
    if(j < 5)  {axis(2,at=0:10,labels=c("0",NA,NA,NA,NA,"5",NA,NA,NA,NA,"10"), las=1)  # right hand
        } else {axis(2,at=0:10,labels=F)}
    if((j %% 4)==0){axis(1,at=1:6,labels=jc)} else {axis(1,labels=F)};  # bottom by modular arithmetic
}
mtext("Collaboration Interest Score",2, outer=T,line=0.5)
mtext("Job Type",                    1, outer=T,line=0.75)
mtext("Pacific Salmon",              3, outer=T,line=-0.25)

 [1] "Field Data"                          
 [2] "Data Analysis"                       
 [3] "Fishery Management, Assessment"      
 [4] "Habitat Assessment"                  
 [5] "Marine Survival, Growth, Migration"  
 [6] "Interactions: Wild, Hatchery, Farmed"
 [7] "Climate and Ecosystem Models"        
 [8] "Field methods"                       
 [9] "Implementation"                      
[10] "First Nations Opportunities"         
[11] "Community engagement"                
[12] "Better science communication"        
[13] "Traditional ecological knowledge"    
[14] "Data sharing arrangements"           
[15] "Celebrating success"                 
[16] "Outreach methods, awareness"         

Atlantic Region

The same topics were applied to the four regions with Atlantic Salmon. That lumped: Quebec, Gulf, Maritimes, Newfoundland. Job type RM was omitted (1 response).

SetPar();par(xaxs="r");
par(mfcol=c(4,4), mar=c(1,1,1,0) );
for(j in 1:nt){
    y=ASJS[top[j],c(1,2,4:6)]  # how 5 job types responded to a topic, not RM 
    plot(1:5,y, xlim=c(1,5),ylim=c(-1,11),xaxt="n",yaxt="n",
    xlab="", ylab="",main=mainText[j], cex.main=0.8);
    m=mean(y);    abline(h=m) # a horizontal line at the mean
    segments(1:5, rep(m,5),1:5,y) # connect dot to horizontal line
    axis(3,labels=F); axis(4,at=0:9,labels=F)
    if(j < 5)  {axis(2,at=0:10,labels=c("0",NA,NA,NA,NA,"5",NA,NA,NA,NA,"10"), las=1)  # right hand
        } else {axis(2,at=0:10,labels=F)}
    if((j %% 4)==0){axis(1,at=1:5,labels=jc[c(1,2,4:6)])} else {axis(1,labels=F)};  # bottom by modular arithmetic
}
mtext("Collaboration Interest Score",2, outer=T,line=0.5)
mtext("Job Type",                    1, outer=T,line=0.75)
mtext("Atlantic Salmon",              3, outer=T,line=-0.25)

Amazing how different RE is compared to MA in term of indicated strong potential for collaboration.

Atlantic and Pacific

Compare responses between Atlantic and Pacific by job type.

SetPar();par(xaxs="r");
par(mfcol=c(4,4), mar=c(1,1,1,0) );
for(j in 1:nt){
    y <- PSJS[top[j],1:6]  # how job types responded to a topic
    plot(1:6,y, xlim=c(1,6),ylim=c(-1,11),xaxt="n",yaxt="n",
        xlab="", ylab="",main=mainText[j], cex.main=0.8);
    m=mean(y);    abline(h=m) # a horizontal line at the mean
    segments(1:6, rep(m,6),1:6,y) # connect dot to horizontal line
    y1 <- ASJS[top[j],c(1,2,4:6)]  # no RM
    points(c(1,2,4:6),y1,pch=5,col="red") # skip jobtype at position 3
    m=mean(y1);    abline(h=m, col="red")
     axis(3,labels=F); axis(4,at=0:9,labels=F)
    if(j < 5)  {axis(2,at=0:10,labels=c("0",NA,NA,NA,NA,"5",NA,NA,NA,NA,"10"), las=1)  # right hand
        } else {axis(2,at=0:10,labels=F)}
    if((j %% 4)==0){axis(1,at=1:6,labels=jc)} else {axis(1,labels=F)};  # bottom by modular arithmetic
}
mtext("Collaboration Interest Score",2, outer=T,line=0.5 ); 
mtext("Job Type",                    1, outer=T,line=0.75); 

There are some sharp contrasts within job types between Atlantic and Pacific, in perceptions of opportunities for collaboration. Comparing Atlantic to Pacific:
* MA had less interest in field data, habitat assessment, and data sharing arrangements than their Pacific counterparts;
* EG were more intersted in marine survival/growth/migration and in fishery management/assessment;
* BI were very similar but more interested in field methods.
* RE were also similar but more interested in data sharing arrangements. * HA differed by having much less interested in celebrating success and in outreach methods/awareness.

Correlations Among Job Types within Top Topics

cp <- cor(PSJS[top,1:6]) %>% `^`(2) %>% `*`(100) %>% round(0) %T>% print; 
ca <- cor(ASJS[top,1:6]) %>% `^`(2) %>% `*`(100) %>% round(0) %T>% print; 
cb <- cp # the bottom
for (j in 1:5){
    for(k in (j+1):6 ){
        cb[j,k] <- ca[j,k];
        if( j == 3 | k == 3) cb[j,k] <- NA
    }
}
for (j in 1:6){
    cb[j,j] <- cor(PSJS[top,j], ASJS[top,j]) %>% `^`(2) %>% `*`(100) %>% round(0)
}    
cb[3,3] <- NA
cb
    MA  EG  RM  BI  RE  HA
MA 100   5  23   1  25   0
EG   5 100  32  16   6   2
RM  23  32 100  30  22   2
BI   1  16  30 100   0  16
RE  25   6  22   0 100   5
HA   0   2   2  16   5 100
    MA  EG  RM  BI  RE  HA
MA 100  10  14   4  41   0
EG  10 100   0  11   0  51
RM  14   0 100   0  18   9
BI   4  11   0 100  11   0
RE  41   0  18  11 100   2
HA   0  51   9   0   2 100
   MA EG RM BI RE HA
MA 36 10 NA  4 41  0
EG  5  8 NA 11  0 51
RM 23 32 NA NA NA NA
BI  1 16 30  1 11  0
RE 25  6 22  0 27  2
HA  0  2  2 16  5  5

Selected Topics for Themes

repr=c(0,0,0,1,1,0,0,0,0,2,2,0,0,0,3,0,0,0,0,0,3,4,0,4,0,0,0,0,5,0,5,0,0,0,6,0,6)
topic[repr!=0]
 [1] "IYS.1.4 Stock Status Assessment"         
 [2] "IYS.1.5 Habitat Assessment"              
 [3] "IYS.2.1 Freshwater habitats"             
 [4] "IYS.2.2 Marine and Estuarine Habitats"   
 [5] "IYS.3.1 Field methods"                   
 [6] "IYS.3.7 Implementation"                  
 [7] "IYS.4.1 First Nations Opportunities"     
 [8] "IYS.4.3 Community engagement"            
 [9] "IYS.5.1 Database Integration"            
[10] "IYS.5.3 Data sharing arrangements"       
[11] "IYS.6.3 Outreach methods, awareness"     
[12] "IYS.6.5 Linking salmon to climate change"

Two Topics per Theme

An experiment. ranking within job types was similar to considering all topics. Reducing the amount of information to describe collaboration interest by theme was interesting, but not included in the final analysis. EDA!

reprTopicScores <- matrix(nrow=6,ncol=7)
jc=c(jobCode[c(1:3,5:7),1],"ALL")
for(j in 1:7) reprTopicScores[,j] <- tapply(PacScoreJob[,j],repr,mean)[-1]
round(reprTopicScores,1)
a <- reprTopicScores+NA
dimnames(a)=list(Theme=theme,JobCode=jc)
for(j in 1:7)a[,j] <- ScaleTo10(reprTopicScores[,j])
kable( a) # ,col.names=c(jc,"ALL"), row.names=theme )
     [,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,]  1.4  1.5  1.5  1.4  1.6  2.7  1.7
[2,]  1.5  1.4  1.6  1.3  1.7  2.2  1.6
[3,]  1.4  2.2  1.9  1.4  1.9  2.4  1.9
[4,]  1.9  1.9  2.2  1.2  1.4  2.6  1.9
[5,]  1.4  2.2  1.9  1.8  1.2  2.2  1.8
[6,]  1.5  1.5  1.6  1.1  2.1  2.6  1.7
MA EG RM BI RE HA ALL
IYS.1 Status Salmon and Habitats 0 1 0 3 5 10 2
IYS.2 Effects of Changing Habitats 2 0 2 2 6 0 0
IYS.3 New tech and methods 1 10 6 4 8 4 10
IYS.4 Connecting Salmon to People 10 6 10 1 3 8 10
IYS.5 Information Systems 1 10 6 10 0 1 7
IYS.6 Outreach and Communication 2 1 2 0 10 9 5

finis